Initial commit

fbshipit-source-id: ad58e416e3ceeca85fae0583308968d04e78fe0d

tests/test_blending.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import numpy as np
+import unittest
+import torch
+
+from pytorch3d.renderer.blending import (
+    BlendParams,
+    hard_rgb_blend,
+    sigmoid_alpha_blend,
+    softmax_rgb_blend,
+)
+from pytorch3d.renderer.mesh.rasterizer import Fragments
+
+
+def sigmoid_blend_naive(colors, fragments, blend_params):
+    """
+    Naive for loop based implementation of distance based alpha calculation.
+    Only for test purposes.
+    """
+    pix_to_face = fragments.pix_to_face
+    dists = fragments.dists
+    sigma = blend_params.sigma
+
+    N, H, W, K = pix_to_face.shape
+    device = pix_to_face.device
+    pixel_colors = torch.ones((N, H, W, 4), dtype=colors.dtype, device=device)
+
+    for n in range(N):
+        for h in range(H):
+            for w in range(W):
+                alpha = 1.0
+
+                # Loop over k faces and calculate 2D distance based probability
+                # map.
+                for k in range(K):
+                    if pix_to_face[n, h, w, k] >= 0:
+                        prob = torch.sigmoid(-dists[n, h, w, k] / sigma)
+                        alpha *= 1.0 - prob  # cumulative product
+                pixel_colors[n, h, w, :3] = colors[n, h, w, 0, :]
+                pixel_colors[n, h, w, 3] = 1.0 - alpha
+
+    pixel_colors = torch.clamp(pixel_colors, min=0, max=1.0)
+    return torch.flip(pixel_colors, [1])
+
+
+def softmax_blend_naive(colors, fragments, blend_params):
+    """
+    Naive for loop based implementation of softmax blending.
+    Only for test purposes.
+    """
+    pix_to_face = fragments.pix_to_face
+    dists = fragments.dists
+    zbuf = fragments.zbuf
+    sigma = blend_params.sigma
+    gamma = blend_params.gamma
+
+    N, H, W, K = pix_to_face.shape
+    device = pix_to_face.device
+    pixel_colors = torch.ones((N, H, W, 4), dtype=colors.dtype, device=device)
+
+    # Near and far clipping planes
+    zfar = 100.0
+    znear = 1.0
+
+    bk_color = blend_params.background_color
+    if not torch.is_tensor(bk_color):
+        bk_color = torch.tensor(bk_color, dtype=colors.dtype, device=device)
+
+    # Background color component
+    delta = np.exp(1e-10 / gamma) * 1e-10
+    delta = torch.tensor(delta).to(device=device)
+
+    for n in range(N):
+        for h in range(H):
+            for w in range(W):
+                alpha = 1.0
+                weights_k = torch.zeros(K)
+                zmax = 0.0
+
+                # Loop over K to find max z.
+                for k in range(K):
+                    if pix_to_face[n, h, w, k] >= 0:
+                        zinv = (zfar - zbuf[n, h, w, k]) / (zfar - znear)
+                        if zinv > zmax:
+                            zmax = zinv
+
+                # Loop over K faces to calculate 2D distance based probability
+                # map and zbuf based weights for colors.
+                for k in range(K):
+                    if pix_to_face[n, h, w, k] >= 0:
+                        zinv = (zfar - zbuf[n, h, w, k]) / (zfar - znear)
+                        prob = torch.sigmoid(-dists[n, h, w, k] / sigma)
+                        alpha *= 1.0 - prob  # cumulative product
+                        weights_k[k] = prob * torch.exp((zinv - zmax) / gamma)
+
+                denom = weights_k.sum() + delta
+                weights = weights_k / denom
+                cols = (weights[..., None] * colors[n, h, w, :, :]).sum(dim=0)
+                pixel_colors[n, h, w, :3] = cols
+                pixel_colors[n, h, w, :3] += (delta / denom) * bk_color
+                pixel_colors[n, h, w, 3] = 1.0 - alpha
+
+    pixel_colors = torch.clamp(pixel_colors, min=0, max=1.0)
+    return torch.flip(pixel_colors, [1])
+
+
+class TestBlending(unittest.TestCase):
+    def setUp(self) -> None:
+        torch.manual_seed(42)
+
+    def test_hard_rgb_blend(self):
+        N, H, W, K = 5, 10, 10, 20
+        pix_to_face = torch.ones((N, H, W, K))
+        bary_coords = torch.ones((N, H, W, K, 3))
+        fragments = Fragments(
+            pix_to_face=pix_to_face,
+            bary_coords=bary_coords,
+            zbuf=pix_to_face,  # dummy
+            dists=pix_to_face,  # dummy
+        )
+        colors = bary_coords.clone()
+        top_k = torch.randn((K, 3))
+        colors[..., :, :] = top_k
+        images = hard_rgb_blend(colors, fragments)
+        expected_vals = torch.ones((N, H, W, 4))
+        pix_cols = torch.ones_like(expected_vals[..., :3]) * top_k[0, :]
+        expected_vals[..., :3] = pix_cols
+        self.assertTrue(torch.allclose(images, expected_vals))
+
+    def test_sigmoid_alpha_blend(self):
+        """
+        Test outputs of sigmoid alpha blend tensorised function match those of
+        the naive iterative version. Also check gradients match.
+        """
+
+        # Create dummy outputs of rasterization simulating a cube in the centre
+        # of the image with surrounding padded values.
+        N, S, K = 1, 8, 2
+        pix_to_face = -torch.ones((N, S, S, K), dtype=torch.int64)
+        h = int(S / 2)
+        pix_to_face_full = torch.randint(size=(N, h, h, K), low=0, high=100)
+        s = int(S / 4)
+        e = int(0.75 * S)
+        pix_to_face[:, s:e, s:e, :] = pix_to_face_full
+        bary_coords = torch.ones((N, S, S, K, 3))
+
+        # randomly flip the sign of the distance
+        # (-) means inside triangle, (+) means outside triangle.
+        random_sign_flip = torch.rand((N, S, S, K))
+        random_sign_flip[random_sign_flip > 0.5] *= -1.0
+        dists = torch.randn(size=(N, S, S, K))
+        dists1 = dists * random_sign_flip
+        dists2 = dists1.clone()
+        dists1.requires_grad = True
+        dists2.requires_grad = True
+        colors = torch.randn_like(bary_coords)
+        fragments1 = Fragments(
+            pix_to_face=pix_to_face,
+            bary_coords=bary_coords,  # dummy
+            zbuf=pix_to_face,  # dummy
+            dists=dists1,
+        )
+        fragments2 = Fragments(
+            pix_to_face=pix_to_face,
+            bary_coords=bary_coords,  # dummy
+            zbuf=pix_to_face,  # dummy
+            dists=dists2,
+        )
+        blend_params = BlendParams(sigma=2e-1)
+        images = sigmoid_alpha_blend(colors, fragments1, blend_params)
+        images_naive = sigmoid_blend_naive(colors, fragments2, blend_params)
+        self.assertTrue(torch.allclose(images, images_naive))
+
+        torch.manual_seed(231)
+        images.sum().backward()
+        self.assertTrue(hasattr(dists1, "grad"))
+        images_naive.sum().backward()
+        self.assertTrue(hasattr(dists2, "grad"))
+
+        self.assertTrue(torch.allclose(dists1.grad, dists2.grad, rtol=1e-5))
+
+    def test_softmax_rgb_blend(self):
+        # Create dummy outputs of rasterization simulating a cube in the centre
+        # of the image with surrounding padded values.
+        N, S, K = 1, 8, 2
+        pix_to_face = -torch.ones((N, S, S, K), dtype=torch.int64)
+        h = int(S / 2)
+        pix_to_face_full = torch.randint(size=(N, h, h, K), low=0, high=100)
+        s = int(S / 4)
+        e = int(0.75 * S)
+        pix_to_face[:, s:e, s:e, :] = pix_to_face_full
+        bary_coords = torch.ones((N, S, S, K, 3))
+
+        random_sign_flip = torch.rand((N, S, S, K))
+        random_sign_flip[random_sign_flip > 0.5] *= -1.0
+        zbuf1 = torch.randn(size=(N, S, S, K))
+
+        # randomly flip the sign of the distance
+        # (-) means inside triangle, (+) means outside triangle.
+        dists1 = torch.randn(size=(N, S, S, K)) * random_sign_flip
+        dists2 = dists1.clone()
+        zbuf2 = zbuf1.clone()
+        dists1.requires_grad = True
+        dists2.requires_grad = True
+        zbuf1.requires_grad = True
+        zbuf2.requires_grad = True
+        colors = torch.randn_like(bary_coords)
+        fragments1 = Fragments(
+            pix_to_face=pix_to_face,
+            bary_coords=bary_coords,  # dummy
+            zbuf=zbuf1,
+            dists=dists1,
+        )
+        fragments2 = Fragments(
+            pix_to_face=pix_to_face,
+            bary_coords=bary_coords,  # dummy
+            zbuf=zbuf2,
+            dists=dists2,
+        )
+        blend_params = BlendParams(sigma=1e-1)
+        images = softmax_rgb_blend(colors, fragments1, blend_params)
+        images_naive = softmax_blend_naive(colors, fragments2, blend_params)
+        self.assertTrue(torch.allclose(images, images_naive))
+
+        # Check gradients.
+        images.sum().backward()
+        self.assertTrue(hasattr(dists1, "grad"))
+        self.assertTrue(hasattr(zbuf1, "grad"))
+        images_naive.sum().backward()
+        self.assertTrue(hasattr(dists2, "grad"))
+        self.assertTrue(hasattr(zbuf2, "grad"))
+
+        self.assertTrue(torch.allclose(dists1.grad, dists2.grad, atol=2e-5))
+        self.assertTrue(torch.allclose(zbuf1.grad, zbuf2.grad, atol=2e-5))

tests/test_cameras.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+# Some of the code below is adapted from Soft Rasterizer (SoftRas)
+#
+# Copyright (c) 2017 Hiroharu Kato
+# Copyright (c) 2018 Nikos Kolotouros
+# Copyright (c) 2019 Shichen Liu
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import math
+import numpy as np
+import unittest
+import torch
+
+from pytorch3d.renderer.cameras import (
+    OpenGLOrthographicCameras,
+    OpenGLPerspectiveCameras,
+    SfMOrthographicCameras,
+    SfMPerspectiveCameras,
+    camera_position_from_spherical_angles,
+    get_world_to_view_transform,
+    look_at_rotation,
+)
+from pytorch3d.transforms import Transform3d
+from pytorch3d.transforms.so3 import so3_exponential_map
+
+from common_testing import TestCaseMixin
+
+
+# Naive function adapted from SoftRasterizer for test purposes.
+def perspective_project_naive(points, fov=60.0):
+    """
+    Compute perspective projection from a given viewing angle.
+    Args:
+        points: (N, V, 3) representing the padded points.
+        viewing angle: degrees
+    Returns:
+        (N, V, 3) tensor of projected points preserving the view space z
+        coordinate (no z renormalization)
+    """
+    device = points.device
+    halfFov = torch.tensor(
+        (fov / 2) / 180 * np.pi, dtype=torch.float32, device=device
+    )
+    scale = torch.tan(halfFov[None])
+    scale = scale[:, None]
+    z = points[:, :, 2]
+    x = points[:, :, 0] / z / scale
+    y = points[:, :, 1] / z / scale
+    points = torch.stack((x, y, z), dim=2)
+    return points
+
+
+def sfm_perspective_project_naive(points, fx=1.0, fy=1.0, p0x=0.0, p0y=0.0):
+    """
+    Compute perspective projection using focal length and principal point.
+
+    Args:
+        points: (N, V, 3) representing the padded points.
+        fx: world units
+        fy: world units
+        p0x: pixels
+        p0y: pixels
+    Returns:
+        (N, V, 3) tensor of projected points.
+    """
+    z = points[:, :, 2]
+    x = (points[:, :, 0] * fx + p0x) / z
+    y = (points[:, :, 1] * fy + p0y) / z
+    points = torch.stack((x, y, 1.0 / z), dim=2)
+    return points
+
+
+# Naive function adapted from SoftRasterizer for test purposes.
+def orthographic_project_naive(points, scale_xyz=(1.0, 1.0, 1.0)):
+    """
+    Compute orthographic projection from a given angle
+    Args:
+        points: (N, V, 3) representing the padded points.
+        scaled: (N, 3) scaling factors for each of xyz directions
+    Returns:
+        (N, V, 3) tensor of projected points preserving the view space z
+        coordinate (no z renormalization).
+    """
+    if not torch.is_tensor(scale_xyz):
+        scale_xyz = torch.tensor(scale_xyz)
+    scale_xyz = scale_xyz.view(-1, 3)
+    z = points[:, :, 2]
+    x = points[:, :, 0] * scale_xyz[:, 0]
+    y = points[:, :, 1] * scale_xyz[:, 1]
+    points = torch.stack((x, y, z), dim=2)
+    return points
+
+
+class TestCameraHelpers(unittest.TestCase):
+    def setUp(self) -> None:
+        super().setUp()
+        torch.manual_seed(42)
+        np.random.seed(42)
+
+    def test_camera_position_from_angles_python_scalar(self):
+        dist = 2.7
+        elev = 90.0
+        azim = 0.0
+        expected_position = torch.tensor(
+            [0.0, 2.7, 0.0], dtype=torch.float32
+        ).view(1, 3)
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
+
+    def test_camera_position_from_angles_python_scalar_radians(self):
+        dist = 2.7
+        elev = math.pi / 2
+        azim = 0.0
+        expected_position = torch.tensor([0.0, 2.7, 0.0], dtype=torch.float32)
+        expected_position = expected_position.view(1, 3)
+        position = camera_position_from_spherical_angles(
+            dist, elev, azim, degrees=False
+        )
+        self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
+
+    def test_camera_position_from_angles_torch_scalars(self):
+        dist = torch.tensor(2.7)
+        elev = torch.tensor(0.0)
+        azim = torch.tensor(90.0)
+        expected_position = torch.tensor(
+            [2.7, 0.0, 0.0], dtype=torch.float32
+        ).view(1, 3)
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
+
+    def test_camera_position_from_angles_mixed_scalars(self):
+        dist = 2.7
+        elev = torch.tensor(0.0)
+        azim = 90.0
+        expected_position = torch.tensor(
+            [2.7, 0.0, 0.0], dtype=torch.float32
+        ).view(1, 3)
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
+
+    def test_camera_position_from_angles_torch_scalar_grads(self):
+        dist = torch.tensor(2.7, requires_grad=True)
+        elev = torch.tensor(45.0, requires_grad=True)
+        azim = torch.tensor(45.0)
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        position.sum().backward()
+        self.assertTrue(hasattr(elev, "grad"))
+        self.assertTrue(hasattr(dist, "grad"))
+        elev_grad = elev.grad.clone()
+        dist_grad = dist.grad.clone()
+        elev = math.pi / 180.0 * elev.detach()
+        azim = math.pi / 180.0 * azim
+        grad_dist = (
+            torch.cos(elev) * torch.sin(azim)
+            + torch.sin(elev)
+            - torch.cos(elev) * torch.cos(azim)
+        )
+        grad_elev = (
+            -torch.sin(elev) * torch.sin(azim)
+            + torch.cos(elev)
+            + torch.sin(elev) * torch.cos(azim)
+        )
+        grad_elev = dist * (math.pi / 180.0) * grad_elev
+        self.assertTrue(torch.allclose(elev_grad, grad_elev))
+        self.assertTrue(torch.allclose(dist_grad, grad_dist))
+
+    def test_camera_position_from_angles_vectors(self):
+        dist = torch.tensor([2.0, 2.0])
+        elev = torch.tensor([0.0, 90.0])
+        azim = torch.tensor([90.0, 0.0])
+        expected_position = torch.tensor(
+            [[2.0, 0.0, 0.0], [0.0, 2.0, 0.0]], dtype=torch.float32
+        )
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
+
+    def test_camera_position_from_angles_vectors_broadcast(self):
+        dist = torch.tensor([2.0, 3.0, 5.0])
+        elev = torch.tensor([0.0])
+        azim = torch.tensor([90.0])
+        expected_position = torch.tensor(
+            [[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]],
+            dtype=torch.float32,
+        )
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        self.assertTrue(torch.allclose(position, expected_position, atol=3e-7))
+
+    def test_camera_position_from_angles_vectors_mixed_broadcast(self):
+        dist = torch.tensor([2.0, 3.0, 5.0])
+        elev = 0.0
+        azim = torch.tensor(90.0)
+        expected_position = torch.tensor(
+            [[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]],
+            dtype=torch.float32,
+        )
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        self.assertTrue(torch.allclose(position, expected_position, atol=3e-7))
+
+    def test_camera_position_from_angles_vectors_mixed_broadcast_grads(self):
+        dist = torch.tensor([2.0, 3.0, 5.0], requires_grad=True)
+        elev = torch.tensor(45.0, requires_grad=True)
+        azim = 45.0
+        position = camera_position_from_spherical_angles(dist, elev, azim)
+        position.sum().backward()
+        self.assertTrue(hasattr(elev, "grad"))
+        self.assertTrue(hasattr(dist, "grad"))
+        elev_grad = elev.grad.clone()
+        dist_grad = dist.grad.clone()
+        azim = torch.tensor(azim)
+        elev = math.pi / 180.0 * elev.detach()
+        azim = math.pi / 180.0 * azim
+        grad_dist = (
+            torch.cos(elev) * torch.sin(azim)
+            + torch.sin(elev)
+            - torch.cos(elev) * torch.cos(azim)
+        )
+        grad_elev = (
+            -torch.sin(elev) * torch.sin(azim)
+            + torch.cos(elev)
+            + torch.sin(elev) * torch.cos(azim)
+        )
+        grad_elev = (dist * (math.pi / 180.0) * grad_elev).sum()
+        self.assertTrue(torch.allclose(elev_grad, grad_elev))
+        self.assertTrue(torch.allclose(dist_grad, grad_dist))
+
+    def test_camera_position_from_angles_vectors_bad_broadcast(self):
+        # Batch dim for broadcast must be N or 1
+        dist = torch.tensor([2.0, 3.0, 5.0])
+        elev = torch.tensor([0.0, 90.0])
+        azim = torch.tensor([90.0])
+        with self.assertRaises(ValueError):
+            camera_position_from_spherical_angles(dist, elev, azim)
+
+    def test_look_at_rotation_python_list(self):
+        camera_position = [[0.0, 0.0, -1.0]]  # camera pointing along negative z
+        rot_mat = look_at_rotation(camera_position)
+        self.assertTrue(torch.allclose(rot_mat, torch.eye(3)[None], atol=2e-7))
+
+    def test_look_at_rotation_input_fail(self):
+        camera_position = [-1.0]  # expected to have xyz positions
+        with self.assertRaises(ValueError):
+            look_at_rotation(camera_position)
+
+    def test_look_at_rotation_list_broadcast(self):
+        # fmt: off
+        camera_positions = [[0.0, 0.0, -1.0], [0.0, 0.0, 1.0]]
+        rot_mats_expected = torch.tensor(
+            [
+                [
+                    [1.0, 0.0, 0.0],
+                    [0.0, 1.0, 0.0],
+                    [0.0, 0.0, 1.0]
+                ],
+                [
+                    [-1.0, 0.0,  0.0],  # noqa: E241, E201
+                    [ 0.0, 1.0,  0.0],  # noqa: E241, E201
+                    [ 0.0, 0.0, -1.0]   # noqa: E241, E201
+                ],
+            ],
+            dtype=torch.float32
+        )
+        # fmt: on
+        rot_mats = look_at_rotation(camera_positions)
+        self.assertTrue(torch.allclose(rot_mats, rot_mats_expected, atol=2e-7))
+
+    def test_look_at_rotation_tensor_broadcast(self):
+        # fmt: off
+        camera_positions = torch.tensor([
+            [0.0, 0.0, -1.0],
+            [0.0, 0.0,  1.0]   # noqa: E241, E201
+        ], dtype=torch.float32)
+        rot_mats_expected = torch.tensor(
+            [
+                [
+                    [1.0, 0.0, 0.0],
+                    [0.0, 1.0, 0.0],
+                    [0.0, 0.0, 1.0]
+                ],
+                [
+                    [-1.0, 0.0,  0.0],  # noqa: E241, E201
+                    [ 0.0, 1.0,  0.0],  # noqa: E241, E201
+                    [ 0.0, 0.0, -1.0]   # noqa: E241, E201
+                ],
+            ],
+            dtype=torch.float32
+        )
+        # fmt: on
+        rot_mats = look_at_rotation(camera_positions)
+        self.assertTrue(torch.allclose(rot_mats, rot_mats_expected, atol=2e-7))
+
+    def test_look_at_rotation_tensor_grad(self):
+        camera_position = torch.tensor([[0.0, 0.0, -1.0]], requires_grad=True)
+        rot_mat = look_at_rotation(camera_position)
+        rot_mat.sum().backward()
+        self.assertTrue(hasattr(camera_position, "grad"))
+        self.assertTrue(
+            torch.allclose(
+                camera_position.grad,
+                torch.zeros_like(camera_position),
+                atol=2e-7,
+            )
+        )
+
+    def test_view_transform(self):
+        T = torch.tensor([0.0, 0.0, -1.0], requires_grad=True).view(1, -1)
+        R = look_at_rotation(T)
+        RT = get_world_to_view_transform(R=R, T=T)
+        self.assertTrue(isinstance(RT, Transform3d))
+
+    def test_view_transform_class_method(self):
+        T = torch.tensor([0.0, 0.0, -1.0], requires_grad=True).view(1, -1)
+        R = look_at_rotation(T)
+        RT = get_world_to_view_transform(R=R, T=T)
+        for cam_type in (
+            OpenGLPerspectiveCameras,
+            OpenGLOrthographicCameras,
+            SfMOrthographicCameras,
+            SfMPerspectiveCameras,
+        ):
+            cam = cam_type(R=R, T=T)
+            RT_class = cam.get_world_to_view_transform()
+            self.assertTrue(
+                torch.allclose(RT.get_matrix(), RT_class.get_matrix())
+            )
+
+        self.assertTrue(isinstance(RT, Transform3d))
+
+    def test_get_camera_center(self, batch_size=10):
+        T = torch.randn(batch_size, 3)
+        R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
+        for cam_type in (
+            OpenGLPerspectiveCameras,
+            OpenGLOrthographicCameras,
+            SfMOrthographicCameras,
+            SfMPerspectiveCameras,
+        ):
+            cam = cam_type(R=R, T=T)
+            C = cam.get_camera_center()
+            C_ = -torch.bmm(R, T[:, :, None])[:, :, 0]
+            self.assertTrue(torch.allclose(C, C_, atol=1e-05))
+
+
+class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
+    def test_perspective(self):
+        far = 10.0
+        near = 1.0
+        cameras = OpenGLPerspectiveCameras(znear=near, zfar=far, fov=60.0)
+        P = cameras.get_projection_transform()
+        # vertices are at the far clipping plane so z gets mapped to 1.
+        vertices = torch.tensor([1, 2, far], dtype=torch.float32)
+        projected_verts = torch.tensor(
+            [np.sqrt(3) / far, 2 * np.sqrt(3) / far, 1.0], dtype=torch.float32
+        )
+        vertices = vertices[None, None, :]
+        v1 = P.transform_points(vertices)
+        v2 = perspective_project_naive(vertices, fov=60.0)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(far * v1[..., 2], v2[..., 2]))
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+        # vertices are at the near clipping plane so z gets mapped to 0.0.
+        vertices[..., 2] = near
+        projected_verts = torch.tensor(
+            [np.sqrt(3) / near, 2 * np.sqrt(3) / near, 0.0], dtype=torch.float32
+        )
+        v1 = P.transform_points(vertices)
+        v2 = perspective_project_naive(vertices, fov=60.0)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+    def test_perspective_kwargs(self):
+        cameras = OpenGLPerspectiveCameras(znear=5.0, zfar=100.0, fov=0.0)
+        # Override defaults by passing in values to get_projection_transform
+        far = 10.0
+        P = cameras.get_projection_transform(znear=1.0, zfar=far, fov=60.0)
+        vertices = torch.tensor([1, 2, far], dtype=torch.float32)
+        projected_verts = torch.tensor(
+            [np.sqrt(3) / far, 2 * np.sqrt(3) / far, 1.0], dtype=torch.float32
+        )
+        vertices = vertices[None, None, :]
+        v1 = P.transform_points(vertices)
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+    def test_perspective_mixed_inputs_broadcast(self):
+        far = torch.tensor([10.0, 20.0], dtype=torch.float32)
+        near = 1.0
+        fov = torch.tensor(60.0)
+        cameras = OpenGLPerspectiveCameras(znear=near, zfar=far, fov=fov)
+        P = cameras.get_projection_transform()
+        vertices = torch.tensor([1, 2, 10], dtype=torch.float32)
+        z1 = 1.0  # vertices at far clipping plane so z = 1.0
+        z2 = (20.0 / (20.0 - 1.0) * 10.0 + -(20.0) / (20.0 - 1.0)) / 10.0
+        projected_verts = torch.tensor(
+            [
+                [np.sqrt(3) / 10.0, 2 * np.sqrt(3) / 10.0, z1],
+                [np.sqrt(3) / 10.0, 2 * np.sqrt(3) / 10.0, z2],
+            ],
+            dtype=torch.float32,
+        )
+        vertices = vertices[None, None, :]
+        v1 = P.transform_points(vertices)
+        v2 = perspective_project_naive(vertices, fov=60.0)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+    def test_perspective_mixed_inputs_grad(self):
+        far = torch.tensor([10.0])
+        near = 1.0
+        fov = torch.tensor(60.0, requires_grad=True)
+        cameras = OpenGLPerspectiveCameras(znear=near, zfar=far, fov=fov)
+        P = cameras.get_projection_transform()
+        vertices = torch.tensor([1, 2, 10], dtype=torch.float32)
+        vertices_batch = vertices[None, None, :]
+        v1 = P.transform_points(vertices_batch).squeeze()
+        v1.sum().backward()
+        self.assertTrue(hasattr(fov, "grad"))
+        fov_grad = fov.grad.clone()
+        half_fov_rad = (math.pi / 180.0) * fov.detach() / 2.0
+        grad_cotan = -(1.0 / (torch.sin(half_fov_rad) ** 2.0) * 1 / 2.0)
+        grad_fov = (math.pi / 180.0) * grad_cotan
+        grad_fov = (vertices[0] + vertices[1]) * grad_fov / 10.0
+        self.assertTrue(torch.allclose(fov_grad, grad_fov))
+
+    def test_camera_class_init(self):
+        device = torch.device("cuda:0")
+        cam = OpenGLPerspectiveCameras(znear=10.0, zfar=(100.0, 200.0))
+
+        # Check broadcasting
+        self.assertTrue(cam.znear.shape == (2,))
+        self.assertTrue(cam.zfar.shape == (2,))
+
+        # update znear element 1
+        cam[1].znear = 20.0
+        self.assertTrue(cam.znear[1] == 20.0)
+
+        # Get item and get value
+        c0 = cam[0]
+        self.assertTrue(c0.zfar == 100.0)
+
+        # Test to
+        new_cam = cam.to(device=device)
+        self.assertTrue(new_cam.device == device)
+
+    def test_get_full_transform(self):
+        cam = OpenGLPerspectiveCameras()
+        T = torch.tensor([0.0, 0.0, 1.0]).view(1, -1)
+        R = look_at_rotation(T)
+        P = cam.get_full_projection_transform(R=R, T=T)
+        self.assertTrue(isinstance(P, Transform3d))
+        self.assertTrue(torch.allclose(cam.R, R))
+        self.assertTrue(torch.allclose(cam.T, T))
+
+    def test_transform_points(self):
+        # Check transform_points methods works with default settings for
+        # RT and P
+        far = 10.0
+        cam = OpenGLPerspectiveCameras(znear=1.0, zfar=far, fov=60.0)
+        points = torch.tensor([1, 2, far], dtype=torch.float32)
+        points = points.view(1, 1, 3).expand(5, 10, -1)
+        projected_points = torch.tensor(
+            [np.sqrt(3) / far, 2 * np.sqrt(3) / far, 1.0], dtype=torch.float32
+        )
+        projected_points = projected_points.view(1, 1, 3).expand(5, 10, -1)
+        new_points = cam.transform_points(points)
+        self.assertTrue(torch.allclose(new_points, projected_points))
+
+
+class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
+    def test_orthographic(self):
+        far = 10.0
+        near = 1.0
+        cameras = OpenGLOrthographicCameras(znear=near, zfar=far)
+        P = cameras.get_projection_transform()
+
+        vertices = torch.tensor([1, 2, far], dtype=torch.float32)
+        projected_verts = torch.tensor([1, 2, 1], dtype=torch.float32)
+        vertices = vertices[None, None, :]
+        v1 = P.transform_points(vertices)
+        v2 = orthographic_project_naive(vertices)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+        vertices[..., 2] = near
+        projected_verts[2] = 0.0
+        v1 = P.transform_points(vertices)
+        v2 = orthographic_project_naive(vertices)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+    def test_orthographic_scaled(self):
+        vertices = torch.tensor([1, 2, 0.5], dtype=torch.float32)
+        vertices = vertices[None, None, :]
+        scale = torch.tensor([[2.0, 0.5, 20]])
+        # applying the scale puts the z coordinate at the far clipping plane
+        # so the z is mapped to 1.0
+        projected_verts = torch.tensor([2, 1, 1], dtype=torch.float32)
+        cameras = OpenGLOrthographicCameras(
+            znear=1.0, zfar=10.0, scale_xyz=scale
+        )
+        P = cameras.get_projection_transform()
+        v1 = P.transform_points(vertices)
+        v2 = orthographic_project_naive(vertices, scale)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1, projected_verts))
+
+    def test_orthographic_kwargs(self):
+        cameras = OpenGLOrthographicCameras(znear=5.0, zfar=100.0)
+        far = 10.0
+        P = cameras.get_projection_transform(znear=1.0, zfar=far)
+        vertices = torch.tensor([1, 2, far], dtype=torch.float32)
+        projected_verts = torch.tensor([1, 2, 1], dtype=torch.float32)
+        vertices = vertices[None, None, :]
+        v1 = P.transform_points(vertices)
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+    def test_orthographic_mixed_inputs_broadcast(self):
+        far = torch.tensor([10.0, 20.0])
+        near = 1.0
+        cameras = OpenGLOrthographicCameras(znear=near, zfar=far)
+        P = cameras.get_projection_transform()
+
+        vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
+        z2 = 1.0 / (20.0 - 1.0) * 10.0 + -(1.0) / (20.0 - 1.0)
+        projected_verts = torch.tensor(
+            [[1.0, 2.0, 1.0], [1.0, 2.0, z2]], dtype=torch.float32
+        )
+        vertices = vertices[None, None, :]
+        v1 = P.transform_points(vertices)
+        v2 = orthographic_project_naive(vertices)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1.squeeze(), projected_verts))
+
+    def test_orthographic_mixed_inputs_grad(self):
+        far = torch.tensor([10.0])
+        near = 1.0
+        scale = torch.tensor([[1.0, 1.0, 1.0]], requires_grad=True)
+        cameras = OpenGLOrthographicCameras(
+            znear=near, zfar=far, scale_xyz=scale
+        )
+        P = cameras.get_projection_transform()
+        vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
+        vertices_batch = vertices[None, None, :]
+        v1 = P.transform_points(vertices_batch)
+        v1.sum().backward()
+        self.assertTrue(hasattr(scale, "grad"))
+        scale_grad = scale.grad.clone()
+        grad_scale = torch.tensor(
+            [
+                [
+                    vertices[0] * P._matrix[:, 0, 0],
+                    vertices[1] * P._matrix[:, 1, 1],
+                    vertices[2] * P._matrix[:, 2, 2],
+                ]
+            ]
+        )
+        self.assertTrue(torch.allclose(scale_grad, grad_scale))
+
+
+class TestSfMOrthographicProjection(TestCaseMixin, unittest.TestCase):
+    def test_orthographic(self):
+        cameras = SfMOrthographicCameras()
+        P = cameras.get_projection_transform()
+
+        vertices = torch.randn([3, 4, 3], dtype=torch.float32)
+        projected_verts = vertices.clone()
+        v1 = P.transform_points(vertices)
+        v2 = orthographic_project_naive(vertices)
+
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1, projected_verts))
+
+    def test_orthographic_scaled(self):
+        focal_length_x = 10.0
+        focal_length_y = 15.0
+
+        cameras = SfMOrthographicCameras(
+            focal_length=((focal_length_x, focal_length_y),)
+        )
+        P = cameras.get_projection_transform()
+
+        vertices = torch.randn([3, 4, 3], dtype=torch.float32)
+        projected_verts = vertices.clone()
+        projected_verts[:, :, 0] *= focal_length_x
+        projected_verts[:, :, 1] *= focal_length_y
+        v1 = P.transform_points(vertices)
+        v2 = orthographic_project_naive(
+            vertices, scale_xyz=(focal_length_x, focal_length_y, 1.0)
+        )
+        v3 = cameras.transform_points(vertices)
+        self.assertTrue(torch.allclose(v1[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v3[..., :2], v2[..., :2]))
+        self.assertTrue(torch.allclose(v1, projected_verts))
+
+    def test_orthographic_kwargs(self):
+        cameras = SfMOrthographicCameras(
+            focal_length=5.0, principal_point=((2.5, 2.5),)
+        )
+        P = cameras.get_projection_transform(
+            focal_length=2.0, principal_point=((2.5, 3.5),)
+        )
+        vertices = torch.randn([3, 4, 3], dtype=torch.float32)
+        projected_verts = vertices.clone()
+        projected_verts[:, :, :2] *= 2.0
+        projected_verts[:, :, 0] += 2.5
+        projected_verts[:, :, 1] += 3.5
+        v1 = P.transform_points(vertices)
+        self.assertTrue(torch.allclose(v1, projected_verts))
+
+
+class TestSfMPerspectiveProjection(TestCaseMixin, unittest.TestCase):
+    def test_perspective(self):
+        cameras = SfMPerspectiveCameras()
+        P = cameras.get_projection_transform()
+
+        vertices = torch.randn([3, 4, 3], dtype=torch.float32)
+        v1 = P.transform_points(vertices)
+        v2 = sfm_perspective_project_naive(vertices)
+        self.assertTrue(torch.allclose(v1, v2))
+
+    def test_perspective_scaled(self):
+        focal_length_x = 10.0
+        focal_length_y = 15.0
+        p0x = 15.0
+        p0y = 30.0
+
+        cameras = SfMPerspectiveCameras(
+            focal_length=((focal_length_x, focal_length_y),),
+            principal_point=((p0x, p0y),),
+        )
+        P = cameras.get_projection_transform()
+
+        vertices = torch.randn([3, 4, 3], dtype=torch.float32)
+        v1 = P.transform_points(vertices)
+        v2 = sfm_perspective_project_naive(
+            vertices, fx=focal_length_x, fy=focal_length_y, p0x=p0x, p0y=p0y
+        )
+        v3 = cameras.transform_points(vertices)
+        self.assertTrue(torch.allclose(v1, v2))
+        self.assertTrue(torch.allclose(v3[..., :2], v2[..., :2]))
+
+    def test_perspective_kwargs(self):
+        cameras = SfMPerspectiveCameras(
+            focal_length=5.0, principal_point=((2.5, 2.5),)
+        )
+        P = cameras.get_projection_transform(
+            focal_length=2.0, principal_point=((2.5, 3.5),)
+        )
+        vertices = torch.randn([3, 4, 3], dtype=torch.float32)
+        v1 = P.transform_points(vertices)
+        v2 = sfm_perspective_project_naive(
+            vertices, fx=2.0, fy=2.0, p0x=2.5, p0y=3.5
+        )
+        self.assertTrue(torch.allclose(v1, v2))

tests/test_chamfer.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import unittest
+import torch
+import torch.nn.functional as F
+
+from pytorch3d.loss import chamfer_distance
+
+
+class TestChamfer(unittest.TestCase):
+    @staticmethod
+    def init_pointclouds(batch_size: int = 10, P1: int = 32, P2: int = 64):
+        """
+        Randomly initialize two batches of point clouds of sizes
+        (N, P1, D) and (N, P2, D) and return random normal vectors for
+        each batch of size (N, P1, 3) and (N, P2, 3).
+        """
+        device = torch.device("cuda:0")
+        p1 = torch.rand((batch_size, P1, 3), dtype=torch.float32, device=device)
+        p1_normals = torch.rand(
+            (batch_size, P1, 3), dtype=torch.float32, device=device
+        )
+        p1_normals = p1_normals / p1_normals.norm(dim=2, p=2, keepdim=True)
+        p2 = torch.rand((batch_size, P2, 3), dtype=torch.float32, device=device)
+        p2_normals = torch.rand(
+            (batch_size, P2, 3), dtype=torch.float32, device=device
+        )
+        p2_normals = p2_normals / p2_normals.norm(dim=2, p=2, keepdim=True)
+        weights = torch.rand((batch_size,), dtype=torch.float32, device=device)
+
+        return p1, p2, p1_normals, p2_normals, weights
+
+    @staticmethod
+    def chamfer_distance_naive(p1, p2, p1_normals=None, p2_normals=None):
+        """
+        Naive iterative implementation of nearest neighbor and chamfer distance.
+        Returns lists of the unreduced loss and loss_normals.
+        """
+        N, P1, D = p1.shape
+        P2 = p2.size(1)
+        device = torch.device("cuda:0")
+        return_normals = p1_normals is not None and p2_normals is not None
+        dist = torch.zeros((N, P1, P2), dtype=torch.float32, device=device)
+
+        for n in range(N):
+            for i1 in range(P1):
+                for i2 in range(P2):
+                    dist[n, i1, i2] = torch.sum(
+                        (p1[n, i1, :] - p2[n, i2, :]) ** 2
+                    )
+
+        loss = [
+            torch.min(dist, dim=2)[0],  # (N, P1)
+            torch.min(dist, dim=1)[0],  # (N, P2)
+        ]
+
+        lnorm = [p1.new_zeros(()), p1.new_zeros(())]
+
+        if return_normals:
+            p1_index = dist.argmin(2).view(N, P1, 1).expand(N, P1, 3)
+            p2_index = dist.argmin(1).view(N, P2, 1).expand(N, P2, 3)
+            lnorm1 = 1 - torch.abs(
+                F.cosine_similarity(
+                    p1_normals, p2_normals.gather(1, p1_index), dim=2, eps=1e-6
+                )
+            )
+            lnorm2 = 1 - torch.abs(
+                F.cosine_similarity(
+                    p2_normals, p1_normals.gather(1, p2_index), dim=2, eps=1e-6
+                )
+            )
+            lnorm = [lnorm1, lnorm2]  # [(N, P1), (N, P2)]
+
+        return loss, lnorm
+
+    def test_chamfer_default_no_normals(self):
+        """
+        Compare chamfer loss with naive implementation using default
+        input values and no normals.
+        """
+        N, P1, P2 = 7, 10, 18
+        p1, p2, _, _, weights = TestChamfer.init_pointclouds(N, P1, P2)
+        pred_loss, _ = TestChamfer.chamfer_distance_naive(p1, p2)
+        loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
+        pred_loss = pred_loss[0].sum(1) / P1 + pred_loss[1].sum(1) / P2
+        pred_loss *= weights
+        pred_loss = pred_loss.sum() / weights.sum()
+        self.assertTrue(torch.allclose(loss, pred_loss))
+        self.assertTrue(loss_norm is None)
+
+    def test_chamfer_point_reduction(self):
+        """
+        Compare output of vectorized chamfer loss with naive implementation
+        for point_reduction in ["mean", "sum", "none"] and
+        batch_reduction = "none".
+        """
+        N, P1, P2 = 7, 10, 18
+        p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
+            N, P1, P2
+        )
+
+        pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, p1_normals, p2_normals
+        )
+
+        # point_reduction = "mean".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="none",
+            point_reduction="mean",
+        )
+        pred_loss_mean = pred_loss[0].sum(1) / P1 + pred_loss[1].sum(1) / P2
+        pred_loss_mean *= weights
+        self.assertTrue(torch.allclose(loss, pred_loss_mean))
+
+        pred_loss_norm_mean = (
+            pred_loss_norm[0].sum(1) / P1 + pred_loss_norm[1].sum(1) / P2
+        )
+        pred_loss_norm_mean *= weights
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm_mean))
+
+        # point_reduction = "sum".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="none",
+            point_reduction="sum",
+        )
+        pred_loss_sum = pred_loss[0].sum(1) + pred_loss[1].sum(1)
+        pred_loss_sum *= weights
+        self.assertTrue(torch.allclose(loss, pred_loss_sum))
+
+        pred_loss_norm_sum = pred_loss_norm[0].sum(1) + pred_loss_norm[1].sum(1)
+        pred_loss_norm_sum *= weights
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm_sum))
+
+        # Error when point_reduction = "none" and batch_reduction = "none".
+        with self.assertRaises(ValueError):
+            chamfer_distance(
+                p1,
+                p2,
+                weights=weights,
+                batch_reduction="none",
+                point_reduction="none",
+            )
+
+        # Error when batch_reduction is not in ["none", "mean", "sum"].
+        with self.assertRaises(ValueError):
+            chamfer_distance(p1, p2, weights=weights, batch_reduction="max")
+
+    def test_chamfer_batch_reduction(self):
+        """
+        Compare output of vectorized chamfer loss with naive implementation
+        for batch_reduction in ["mean", "sum"] and point_reduction = "none".
+        """
+        N, P1, P2 = 7, 10, 18
+        p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
+            N, P1, P2
+        )
+
+        pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, p1_normals, p2_normals
+        )
+
+        # batch_reduction = "sum".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="sum",
+            point_reduction="none",
+        )
+        pred_loss[0] *= weights.view(N, 1)
+        pred_loss[1] *= weights.view(N, 1)
+        pred_loss = pred_loss[0].sum() + pred_loss[1].sum()
+        self.assertTrue(torch.allclose(loss, pred_loss))
+
+        pred_loss_norm[0] *= weights.view(N, 1)
+        pred_loss_norm[1] *= weights.view(N, 1)
+        pred_loss_norm = pred_loss_norm[0].sum() + pred_loss_norm[1].sum()
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm))
+
+        # batch_reduction = "mean".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="mean",
+            point_reduction="none",
+        )
+
+        pred_loss /= weights.sum()
+        self.assertTrue(torch.allclose(loss, pred_loss))
+
+        pred_loss_norm /= weights.sum()
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm))
+
+        # Error when point_reduction is not in ["none", "mean", "sum"].
+        with self.assertRaises(ValueError):
+            chamfer_distance(p1, p2, weights=weights, point_reduction="max")
+
+    def test_chamfer_joint_reduction(self):
+        """
+        Compare output of vectorized chamfer loss with naive implementation
+        for batch_reduction in ["mean", "sum"] and
+        point_reduction in ["mean", "sum"].
+        """
+        N, P1, P2 = 7, 10, 18
+        p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
+            N, P1, P2
+        )
+
+        pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, p1_normals, p2_normals
+        )
+
+        # batch_reduction = "sum", point_reduction = "sum".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="sum",
+            point_reduction="sum",
+        )
+        pred_loss[0] *= weights.view(N, 1)
+        pred_loss[1] *= weights.view(N, 1)
+        pred_loss_sum = pred_loss[0].sum(1) + pred_loss[1].sum(1)  # point sum
+        pred_loss_sum = pred_loss_sum.sum()  # batch sum
+        self.assertTrue(torch.allclose(loss, pred_loss_sum))
+
+        pred_loss_norm[0] *= weights.view(N, 1)
+        pred_loss_norm[1] *= weights.view(N, 1)
+        pred_loss_norm_sum = pred_loss_norm[0].sum(1) + pred_loss_norm[1].sum(
+            1
+        )  # point sum.
+        pred_loss_norm_sum = pred_loss_norm_sum.sum()  # batch sum
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm_sum))
+
+        # batch_reduction = "mean", point_reduction = "sum".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="mean",
+            point_reduction="sum",
+        )
+        pred_loss_sum /= weights.sum()
+        self.assertTrue(torch.allclose(loss, pred_loss_sum))
+
+        pred_loss_norm_sum /= weights.sum()
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm_sum))
+
+        # batch_reduction = "sum", point_reduction = "mean".
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="sum",
+            point_reduction="mean",
+        )
+        pred_loss_mean = pred_loss[0].sum(1) / P1 + pred_loss[1].sum(1) / P2
+        pred_loss_mean = pred_loss_mean.sum()
+        self.assertTrue(torch.allclose(loss, pred_loss_mean))
+
+        pred_loss_norm_mean = (
+            pred_loss_norm[0].sum(1) / P1 + pred_loss_norm[1].sum(1) / P2
+        )
+        pred_loss_norm_mean = pred_loss_norm_mean.sum()
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm_mean))
+
+        # batch_reduction = "mean", point_reduction = "mean". This is the default.
+        loss, loss_norm = chamfer_distance(
+            p1,
+            p2,
+            p1_normals,
+            p2_normals,
+            weights=weights,
+            batch_reduction="mean",
+            point_reduction="mean",
+        )
+        pred_loss_mean /= weights.sum()
+        self.assertTrue(torch.allclose(loss, pred_loss_mean))
+
+        pred_loss_norm_mean /= weights.sum()
+        self.assertTrue(torch.allclose(loss_norm, pred_loss_norm_mean))
+
+    def test_incorrect_weights(self):
+        N, P1, P2 = 16, 64, 128
+        device = torch.device("cuda:0")
+        p1 = torch.rand(
+            (N, P1, 3), dtype=torch.float32, device=device, requires_grad=True
+        )
+        p2 = torch.rand(
+            (N, P2, 3), dtype=torch.float32, device=device, requires_grad=True
+        )
+
+        weights = torch.zeros((N,), dtype=torch.float32, device=device)
+        loss, loss_norm = chamfer_distance(
+            p1, p2, weights=weights, batch_reduction="mean"
+        )
+        self.assertTrue(torch.allclose(loss.cpu(), torch.zeros((1,))))
+        self.assertTrue(loss.requires_grad)
+        self.assertTrue(torch.allclose(loss_norm.cpu(), torch.zeros((1,))))
+        self.assertTrue(loss_norm.requires_grad)
+
+        loss, loss_norm = chamfer_distance(
+            p1, p2, weights=weights, batch_reduction="none"
+        )
+        self.assertTrue(torch.allclose(loss.cpu(), torch.zeros((N,))))
+        self.assertTrue(loss.requires_grad)
+        self.assertTrue(torch.allclose(loss_norm.cpu(), torch.zeros((N,))))
+        self.assertTrue(loss_norm.requires_grad)
+
+        weights = torch.ones((N,), dtype=torch.float32, device=device) * -1
+        with self.assertRaises(ValueError):
+            loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
+
+        weights = torch.zeros((N - 1,), dtype=torch.float32, device=device)
+        with self.assertRaises(ValueError):
+            loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
+
+    @staticmethod
+    def chamfer_with_init(
+        batch_size: int, P1: int, P2: int, return_normals: bool
+    ):
+        p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
+            batch_size, P1, P2
+        )
+        torch.cuda.synchronize()
+
+        def loss():
+            loss, loss_normals = chamfer_distance(
+                p1, p2, p1_normals, p2_normals, weights=weights
+            )
+            torch.cuda.synchronize()
+
+        return loss
+
+    @staticmethod
+    def chamfer_naive_with_init(
+        batch_size: int, P1: int, P2: int, return_normals: bool
+    ):
+        p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
+            batch_size, P1, P2
+        )
+        torch.cuda.synchronize()
+
+        def loss():
+            loss, loss_normals = TestChamfer.chamfer_distance_naive(
+                p1, p2, p1_normals, p2_normals
+            )
+            torch.cuda.synchronize()
+
+        return loss

tests/test_cubify.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import unittest
+import torch
+
+from pytorch3d.ops import cubify
+
+
+class TestCubify(unittest.TestCase):
+    def test_allempty(self):
+        N, V = 32, 14
+        device = torch.device("cuda:0")
+        voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
+        meshes = cubify(voxels, 0.5, 0)
+        self.assertTrue(meshes.isempty)
+
+    def test_cubify(self):
+        N, V = 4, 2
+        device = torch.device("cuda:0")
+        voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
+
+        # 1st example: (top left corner, znear) is on
+        voxels[0, 0, 0, 0] = 1.0
+        # 2nd example: all are on
+        voxels[1] = 1.0
+        # 3rd example: empty
+        # 4th example
+        voxels[3, :, :, 1] = 1.0
+        voxels[3, 1, 1, 0] = 1.0
+
+        # compute cubify
+        meshes = cubify(voxels, 0.5, 0)
+
+        # 1st-check
+        verts, faces = meshes.get_mesh_verts_faces(0)
+        self.assertTrue(
+            torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1]))
+        )
+        self.assertTrue(
+            torch.allclose(
+                verts,
+                torch.tensor(
+                    [
+                        [-1.0, -1.0, -1.0],
+                        [-1.0, -1.0, 1.0],
+                        [1.0, -1.0, -1.0],
+                        [1.0, -1.0, 1.0],
+                        [-1.0, 1.0, -1.0],
+                        [-1.0, 1.0, 1.0],
+                        [1.0, 1.0, -1.0],
+                        [1.0, 1.0, 1.0],
+                    ],
+                    dtype=torch.float32,
+                    device=device,
+                ),
+            )
+        )
+        self.assertTrue(
+            torch.allclose(
+                faces,
+                torch.tensor(
+                    [
+                        [0, 1, 4],
+                        [1, 5, 4],
+                        [4, 5, 6],
+                        [5, 7, 6],
+                        [0, 4, 6],
+                        [0, 6, 2],
+                        [0, 3, 1],
+                        [0, 2, 3],
+                        [6, 7, 3],
+                        [6, 3, 2],
+                        [1, 7, 5],
+                        [1, 3, 7],
+                    ],
+                    dtype=torch.int64,
+                    device=device,
+                ),
+            )
+        )
+        # 2nd-check
+        verts, faces = meshes.get_mesh_verts_faces(1)
+        self.assertTrue(
+            torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1]))
+        )
+        self.assertTrue(
+            torch.allclose(
+                verts,
+                torch.tensor(
+                    [
+                        [-1.0, -1.0, -1.0],
+                        [-1.0, -1.0, 1.0],
+                        [-1.0, -1.0, 3.0],
+                        [1.0, -1.0, -1.0],
+                        [1.0, -1.0, 1.0],
+                        [1.0, -1.0, 3.0],
+                        [3.0, -1.0, -1.0],
+                        [3.0, -1.0, 1.0],
+                        [3.0, -1.0, 3.0],
+                        [-1.0, 1.0, -1.0],
+                        [-1.0, 1.0, 1.0],
+                        [-1.0, 1.0, 3.0],
+                        [1.0, 1.0, -1.0],
+                        [1.0, 1.0, 3.0],
+                        [3.0, 1.0, -1.0],
+                        [3.0, 1.0, 1.0],
+                        [3.0, 1.0, 3.0],
+                        [-1.0, 3.0, -1.0],
+                        [-1.0, 3.0, 1.0],
+                        [-1.0, 3.0, 3.0],
+                        [1.0, 3.0, -1.0],
+                        [1.0, 3.0, 1.0],
+                        [1.0, 3.0, 3.0],
+                        [3.0, 3.0, -1.0],
+                        [3.0, 3.0, 1.0],
+                        [3.0, 3.0, 3.0],
+                    ],
+                    dtype=torch.float32,
+                    device=device,
+                ),
+            )
+        )
+        self.assertTrue(
+            torch.allclose(
+                faces,
+                torch.tensor(
+                    [
+                        [0, 1, 9],
+                        [1, 10, 9],
+                        [0, 9, 12],
+                        [0, 12, 3],
+                        [0, 4, 1],
+                        [0, 3, 4],
+                        [1, 2, 10],
+                        [2, 11, 10],
+                        [1, 5, 2],
+                        [1, 4, 5],
+                        [2, 13, 11],
+                        [2, 5, 13],
+                        [3, 12, 14],
+                        [3, 14, 6],
+                        [3, 7, 4],
+                        [3, 6, 7],
+                        [14, 15, 7],
+                        [14, 7, 6],
+                        [4, 8, 5],
+                        [4, 7, 8],
+                        [15, 16, 8],
+                        [15, 8, 7],
+                        [5, 16, 13],
+                        [5, 8, 16],
+                        [9, 10, 17],
+                        [10, 18, 17],
+                        [17, 18, 20],
+                        [18, 21, 20],
+                        [9, 17, 20],
+                        [9, 20, 12],
+                        [10, 11, 18],
+                        [11, 19, 18],
+                        [18, 19, 21],
+                        [19, 22, 21],
+                        [11, 22, 19],
+                        [11, 13, 22],
+                        [20, 21, 23],
+                        [21, 24, 23],
+                        [12, 20, 23],
+                        [12, 23, 14],
+                        [23, 24, 15],
+                        [23, 15, 14],
+                        [21, 22, 24],
+                        [22, 25, 24],
+                        [24, 25, 16],
+                        [24, 16, 15],
+                        [13, 25, 22],
+                        [13, 16, 25],
+                    ],
+                    dtype=torch.int64,
+                    device=device,
+                ),
+            )
+        )
+
+        # 3rd-check
+        verts, faces = meshes.get_mesh_verts_faces(2)
+        self.assertTrue(verts.size(0) == 0)
+        self.assertTrue(faces.size(0) == 0)
+
+        # 4th-check
+        verts, faces = meshes.get_mesh_verts_faces(3)
+        self.assertTrue(
+            torch.allclose(
+                verts,
+                torch.tensor(
+                    [
+                        [1.0, -1.0, -1.0],
+                        [1.0, -1.0, 1.0],
+                        [1.0, -1.0, 3.0],
+                        [3.0, -1.0, -1.0],
+                        [3.0, -1.0, 1.0],
+                        [3.0, -1.0, 3.0],
+                        [-1.0, 1.0, 1.0],
+                        [-1.0, 1.0, 3.0],
+                        [1.0, 1.0, -1.0],
+                        [1.0, 1.0, 1.0],
+                        [1.0, 1.0, 3.0],
+                        [3.0, 1.0, -1.0],
+                        [3.0, 1.0, 1.0],
+                        [3.0, 1.0, 3.0],
+                        [-1.0, 3.0, 1.0],
+                        [-1.0, 3.0, 3.0],
+                        [1.0, 3.0, -1.0],
+                        [1.0, 3.0, 1.0],
+                        [1.0, 3.0, 3.0],
+                        [3.0, 3.0, -1.0],
+                        [3.0, 3.0, 1.0],
+                        [3.0, 3.0, 3.0],
+                    ],
+                    dtype=torch.float32,
+                    device=device,
+                ),
+            )
+        )
+        self.assertTrue(
+            torch.allclose(
+                faces,
+                torch.tensor(
+                    [
+                        [0, 1, 8],
+                        [1, 9, 8],
+                        [0, 8, 11],
+                        [0, 11, 3],
+                        [0, 4, 1],
+                        [0, 3, 4],
+                        [11, 12, 4],
+                        [11, 4, 3],
+                        [1, 2, 9],
+                        [2, 10, 9],
+                        [1, 5, 2],
+                        [1, 4, 5],
+                        [12, 13, 5],
+                        [12, 5, 4],
+                        [2, 13, 10],
+                        [2, 5, 13],
+                        [6, 7, 14],
+                        [7, 15, 14],
+                        [14, 15, 17],
+                        [15, 18, 17],
+                        [6, 14, 17],
+                        [6, 17, 9],
+                        [6, 10, 7],
+                        [6, 9, 10],
+                        [7, 18, 15],
+                        [7, 10, 18],
+                        [8, 9, 16],
+                        [9, 17, 16],
+                        [16, 17, 19],
+                        [17, 20, 19],
+                        [8, 16, 19],
+                        [8, 19, 11],
+                        [19, 20, 12],
+                        [19, 12, 11],
+                        [17, 18, 20],
+                        [18, 21, 20],
+                        [20, 21, 13],
+                        [20, 13, 12],
+                        [10, 21, 18],
+                        [10, 13, 21],
+                    ],
+                    dtype=torch.int64,
+                    device=device,
+                ),
+            )
+        )
+
+    @staticmethod
+    def cubify_with_init(batch_size: int, V: int):
+        device = torch.device("cuda:0")
+        voxels = torch.rand(
+            (batch_size, V, V, V), dtype=torch.float32, device=device
+        )
+        torch.cuda.synchronize()
+
+        def convert():
+            cubify(voxels, 0.5)
+            torch.cuda.synchronize()
+
+        return convert

tests/test_graph_conv.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import unittest
+import torch
+import torch.nn as nn
+
+from pytorch3d import _C
+from pytorch3d.ops.graph_conv import (
+    GraphConv,
+    gather_scatter,
+    gather_scatter_python,
+)
+from pytorch3d.structures.meshes import Meshes
+from pytorch3d.utils import ico_sphere
+
+
+class TestGraphConv(unittest.TestCase):
+    def test_undirected(self):
+        dtype = torch.float32
+        device = torch.device("cuda:0")
+        verts = torch.tensor(
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype, device=device
+        )
+        edges = torch.tensor([[0, 1], [0, 2]], device=device)
+        w0 = torch.tensor([[1, 1, 1]], dtype=dtype, device=device)
+        w1 = torch.tensor([[-1, -1, -1]], dtype=dtype, device=device)
+
+        expected_y = torch.tensor(
+            [
+                [1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9],
+                [4 + 5 + 6 - 1 - 2 - 3],
+                [7 + 8 + 9 - 1 - 2 - 3],
+            ],
+            dtype=dtype,
+            device=device,
+        )
+
+        conv = GraphConv(3, 1, directed=False).to(device)
+        conv.w0.weight.data.copy_(w0)
+        conv.w0.bias.data.zero_()
+        conv.w1.weight.data.copy_(w1)
+        conv.w1.bias.data.zero_()
+
+        y = conv(verts, edges)
+        self.assertTrue(torch.allclose(y, expected_y))
+
+    def test_no_edges(self):
+        dtype = torch.float32
+        verts = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype)
+        edges = torch.zeros(0, 2, dtype=torch.int64)
+        w0 = torch.tensor([[1, -1, -2]], dtype=dtype)
+        expected_y = torch.tensor(
+            [[1 - 2 - 2 * 3], [4 - 5 - 2 * 6], [7 - 8 - 2 * 9]], dtype=dtype
+        )
+        conv = GraphConv(3, 1).to(dtype)
+        conv.w0.weight.data.copy_(w0)
+        conv.w0.bias.data.zero_()
+
+        y = conv(verts, edges)
+        self.assertTrue(torch.allclose(y, expected_y))
+
+    def test_no_verts_and_edges(self):
+        dtype = torch.float32
+        verts = torch.tensor([], dtype=dtype, requires_grad=True)
+        edges = torch.tensor([], dtype=dtype)
+        w0 = torch.tensor([[1, -1, -2]], dtype=dtype)
+        conv = GraphConv(3, 1).to(dtype)
+        conv.w0.weight.data.copy_(w0)
+        conv.w0.bias.data.zero_()
+
+        y = conv(verts, edges)
+        self.assertTrue(torch.allclose(y, torch.tensor([])))
+        self.assertTrue(y.requires_grad)
+
+    def test_directed(self):
+        dtype = torch.float32
+        verts = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype)
+        edges = torch.tensor([[0, 1], [0, 2]])
+        w0 = torch.tensor([[1, 1, 1]], dtype=dtype)
+        w1 = torch.tensor([[-1, -1, -1]], dtype=dtype)
+
+        expected_y = torch.tensor(
+            [[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], [4 + 5 + 6], [7 + 8 + 9]],
+            dtype=dtype,
+        )
+
+        conv = GraphConv(3, 1, directed=True).to(dtype)
+        conv.w0.weight.data.copy_(w0)
+        conv.w0.bias.data.zero_()
+        conv.w1.weight.data.copy_(w1)
+        conv.w1.bias.data.zero_()
+
+        y = conv(verts, edges)
+        self.assertTrue(torch.allclose(y, expected_y))
+
+    def test_backward(self):
+        device = torch.device("cuda:0")
+        mesh = ico_sphere()
+        verts = mesh.verts_packed()
+        edges = mesh.edges_packed()
+        verts_cuda = verts.clone().to(device)
+        edges_cuda = edges.clone().to(device)
+        verts.requires_grad = True
+        verts_cuda.requires_grad = True
+
+        neighbor_sums_cuda = gather_scatter(verts_cuda, edges_cuda, False)
+        neighbor_sums = gather_scatter_python(verts, edges, False)
+        neighbor_sums_cuda.sum().backward()
+        neighbor_sums.sum().backward()
+
+        self.assertTrue(torch.allclose(verts.grad.cpu(), verts_cuda.grad.cpu()))
+
+    def test_repr(self):
+        conv = GraphConv(32, 64, directed=True)
+        self.assertEqual(repr(conv), "GraphConv(32 -> 64, directed=True)")
+
+    def test_cpu_cuda_tensor_error(self):
+        device = torch.device("cuda:0")
+        verts = torch.tensor(
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            dtype=torch.float32,
+            device=device,
+        )
+        edges = torch.tensor([[0, 1], [0, 2]])
+        conv = GraphConv(3, 1, directed=True).to(torch.float32)
+        with self.assertRaises(Exception) as err:
+            conv(verts, edges)
+        self.assertTrue(
+            "tensors must be on the same device." in str(err.exception)
+        )
+
+    def test_gather_scatter(self):
+        """
+        Check gather_scatter cuda and python versions give the same results.
+        Check that gather_scatter cuda version throws an error if cpu tensors
+        are given as input.
+        """
+        device = torch.device("cuda:0")
+        mesh = ico_sphere()
+        verts = mesh.verts_packed()
+        edges = mesh.edges_packed()
+        w0 = nn.Linear(3, 1)
+        input = w0(verts)
+
+        # output
+        output_cpu = gather_scatter_python(input, edges, False)
+        output_cuda = _C.gather_scatter(
+            input.to(device=device), edges.to(device=device), False, False
+        )
+        self.assertTrue(torch.allclose(output_cuda.cpu(), output_cpu))
+        with self.assertRaises(Exception) as err:
+            _C.gather_scatter(input.cpu(), edges.cpu(), False, False)
+        self.assertTrue("Not implemented on the CPU" in str(err.exception))
+
+        # directed
+        output_cpu = gather_scatter_python(input, edges, True)
+        output_cuda = _C.gather_scatter(
+            input.to(device=device), edges.to(device=device), True, False
+        )
+        self.assertTrue(torch.allclose(output_cuda.cpu(), output_cpu))
+
+    @staticmethod
+    def graph_conv_forward_backward(
+        gconv_dim,
+        num_meshes,
+        num_verts,
+        num_faces,
+        directed: bool,
+        backend: str = "cuda",
+    ):
+        device = torch.device("cuda") if backend == "cuda" else "cpu"
+        verts_list = torch.tensor(
+            num_verts * [[0.11, 0.22, 0.33]], device=device
+        ).view(-1, 3)
+        faces_list = torch.tensor(num_faces * [[1, 2, 3]], device=device).view(
+            -1, 3
+        )
+        meshes = Meshes(num_meshes * [verts_list], num_meshes * [faces_list])
+        gconv = GraphConv(gconv_dim, gconv_dim, directed=directed)
+        gconv.to(device)
+        edges = meshes.edges_packed()
+        total_verts = meshes.verts_packed().shape[0]
+
+        # Features.
+        x = torch.randn(
+            total_verts, gconv_dim, device=device, requires_grad=True
+        )
+        torch.cuda.synchronize()
+
+        def run_graph_conv():
+            y1 = gconv(x, edges)
+            y1.sum().backward()
+            torch.cuda.synchronize()
+
+        return run_graph_conv

tests/test_lighting.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import numpy as np
+import unittest
+import torch
+
+from pytorch3d.renderer.lighting import DirectionalLights, PointLights
+from pytorch3d.transforms import RotateAxisAngle
+
+from common_testing import TestCaseMixin
+
+
+class TestLights(TestCaseMixin, unittest.TestCase):
+    def test_init_lights(self):
+        """
+        Initialize Lights class with the default values.
+        """
+        device = torch.device("cuda:0")
+        light = DirectionalLights(device=device)
+        keys = ["ambient_color", "diffuse_color", "specular_color", "direction"]
+        for k in keys:
+            prop = getattr(light, k)
+            self.assertTrue(torch.is_tensor(prop))
+            self.assertTrue(prop.device == device)
+            self.assertTrue(prop.shape == (1, 3))
+
+        light = PointLights(device=device)
+        keys = ["ambient_color", "diffuse_color", "specular_color", "location"]
+        for k in keys:
+            prop = getattr(light, k)
+            self.assertTrue(torch.is_tensor(prop))
+            self.assertTrue(prop.device == device)
+            self.assertTrue(prop.shape == (1, 3))
+
+    def test_lights_clone_to(self):
+        device = torch.device("cuda:0")
+        cpu = torch.device("cpu")
+        light = DirectionalLights()
+        new_light = light.clone().to(device)
+        keys = ["ambient_color", "diffuse_color", "specular_color", "direction"]
+        for k in keys:
+            prop = getattr(light, k)
+            new_prop = getattr(new_light, k)
+            self.assertTrue(prop.device == cpu)
+            self.assertTrue(new_prop.device == device)
+            self.assertSeparate(new_prop, prop)
+
+        light = PointLights()
+        new_light = light.clone().to(device)
+        keys = ["ambient_color", "diffuse_color", "specular_color", "location"]
+        for k in keys:
+            prop = getattr(light, k)
+            new_prop = getattr(new_light, k)
+            self.assertTrue(prop.device == cpu)
+            self.assertTrue(new_prop.device == device)
+            self.assertSeparate(new_prop, prop)
+
+    def test_lights_accessor(self):
+        d_light = DirectionalLights(
+            ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))
+        )
+        p_light = PointLights(ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)))
+        for light in [d_light, p_light]:
+            # Update element
+            color = (0.5, 0.5, 0.5)
+            light[1].ambient_color = color
+            self.assertTrue(
+                torch.allclose(light.ambient_color[1], torch.tensor(color))
+            )
+            # Get item and get value
+            l0 = light[0]
+            self.assertTrue(
+                torch.allclose(l0.ambient_color, torch.tensor((0.0, 0.0, 0.0)))
+            )
+
+    def test_initialize_lights_broadcast(self):
+        light = DirectionalLights(
+            ambient_color=torch.randn(10, 3),
+            diffuse_color=torch.randn(1, 3),
+            specular_color=torch.randn(1, 3),
+        )
+        keys = ["ambient_color", "diffuse_color", "specular_color", "direction"]
+        for k in keys:
+            prop = getattr(light, k)
+            self.assertTrue(prop.shape == (10, 3))
+
+        light = PointLights(
+            ambient_color=torch.randn(10, 3),
+            diffuse_color=torch.randn(1, 3),
+            specular_color=torch.randn(1, 3),
+        )
+        keys = ["ambient_color", "diffuse_color", "specular_color", "location"]
+        for k in keys:
+            prop = getattr(light, k)
+            self.assertTrue(prop.shape == (10, 3))
+
+    def test_initialize_lights_broadcast_fail(self):
+        """
+        Batch dims have to be the same or 1.
+        """
+        with self.assertRaises(ValueError):
+            DirectionalLights(
+                ambient_color=torch.randn(10, 3),
+                diffuse_color=torch.randn(15, 3),
+            )
+
+        with self.assertRaises(ValueError):
+            PointLights(
+                ambient_color=torch.randn(10, 3),
+                diffuse_color=torch.randn(15, 3),
+            )
+
+    def test_initialize_lights_dimensions_fail(self):
+        """
+        Color should have shape (N, 3) or (1, 3)
+        """
+        with self.assertRaises(ValueError):
+            DirectionalLights(ambient_color=torch.randn(10, 4))
+
+        with self.assertRaises(ValueError):
+            DirectionalLights(direction=torch.randn(10, 4))
+
+        with self.assertRaises(ValueError):
+            PointLights(ambient_color=torch.randn(10, 4))
+
+        with self.assertRaises(ValueError):
+            PointLights(location=torch.randn(10, 4))
+
+
+class TestDiffuseLighting(unittest.TestCase):
+    def test_diffuse_directional_lights(self):
+        """
+        Test with a single point where:
+        1) the normal and light direction are 45 degrees apart.
+        2) the normal and light direction are 90 degrees apart. The output
+           should be zero for this case
+        """
+        color = torch.tensor([1, 1, 1], dtype=torch.float32)
+        direction = torch.tensor(
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
+        )
+        normals = torch.tensor([0, 0, 1], dtype=torch.float32)
+        normals = normals[None, None, :]
+        expected_output = torch.tensor(
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
+            dtype=torch.float32,
+        )
+        expected_output = expected_output.view(-1, 1, 3)
+        light = DirectionalLights(diffuse_color=color, direction=direction)
+        output_light = light.diffuse(normals=normals)
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+        # Change light direction to be 90 degrees apart from normal direction.
+        direction = torch.tensor([0, 1, 0], dtype=torch.float32)
+        light.direction = direction
+        expected_output = torch.zeros_like(expected_output)
+        output_light = light.diffuse(normals=normals)
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+    def test_diffuse_point_lights(self):
+        """
+        Test with a single point at the origin. Test two cases:
+        1) the point light is at (1, 0, 1) hence the light direction is 45
+           degrees apart from the normal direction
+        1) the point light is at (0, 1, 0) hence the light direction is 90
+           degrees apart from the normal direction. The output
+           should be zero for this case
+        """
+        color = torch.tensor([1, 1, 1], dtype=torch.float32)
+        location = torch.tensor(
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
+        )
+        points = torch.tensor([0, 0, 0], dtype=torch.float32)
+        normals = torch.tensor([0, 0, 1], dtype=torch.float32)
+        expected_output = torch.tensor(
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
+            dtype=torch.float32,
+        )
+        expected_output = expected_output.view(-1, 1, 3)
+        light = PointLights(
+            diffuse_color=color[None, :], location=location[None, :]
+        )
+        output_light = light.diffuse(
+            points=points[None, None, :], normals=normals[None, None, :]
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+        # Change light direction to be 90 degrees apart from normal direction.
+        location = torch.tensor([0, 1, 0], dtype=torch.float32)
+        expected_output = torch.zeros_like(expected_output)
+        light = PointLights(
+            diffuse_color=color[None, :], location=location[None, :]
+        )
+        output_light = light.diffuse(
+            points=points[None, None, :], normals=normals[None, None, :]
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+    def test_diffuse_batched(self):
+        """
+        Test with a batch where each batch element has one point
+        where the normal and light direction are 45 degrees apart.
+        """
+        batch_size = 10
+        color = torch.tensor([1, 1, 1], dtype=torch.float32)
+        direction = torch.tensor(
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
+        )
+        normals = torch.tensor([0, 0, 1], dtype=torch.float32)
+        expected_out = torch.tensor(
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
+            dtype=torch.float32,
+        )
+
+        # Reshape
+        direction = direction.view(-1, 3).expand(batch_size, -1)
+        normals = normals.view(-1, 1, 3).expand(batch_size, -1, -1)
+        color = color.view(-1, 3).expand(batch_size, -1)
+        expected_out = expected_out.view(-1, 1, 3).expand(batch_size, 1, 3)
+
+        lights = DirectionalLights(diffuse_color=color, direction=direction)
+        output_light = lights.diffuse(normals=normals)
+        self.assertTrue(torch.allclose(output_light, expected_out))
+
+    def test_diffuse_batched_broadcast_inputs(self):
+        """
+        Test with a batch where each batch element has one point
+        where the normal and light direction are 45 degrees apart.
+        The color and direction are the same for each batch element.
+        """
+        batch_size = 10
+        color = torch.tensor([1, 1, 1], dtype=torch.float32)
+        direction = torch.tensor(
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
+        )
+        normals = torch.tensor([0, 0, 1], dtype=torch.float32)
+        expected_out = torch.tensor(
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
+            dtype=torch.float32,
+        )
+
+        # Reshape
+        normals = normals.view(-1, 1, 3).expand(batch_size, -1, -1)
+        expected_out = expected_out.view(-1, 1, 3).expand(batch_size, 1, 3)
+
+        # Don't expand the direction or color. Broadcasting should happen
+        # in the diffuse function.
+        direction = direction.view(1, 3)
+        color = color.view(1, 3)
+
+        lights = DirectionalLights(diffuse_color=color, direction=direction)
+        output_light = lights.diffuse(normals=normals)
+        self.assertTrue(torch.allclose(output_light, expected_out))
+
+    def test_diffuse_batched_arbitrary_input_dims(self):
+        """
+        Test with a batch of inputs where shape of the input is mimicking the
+        shape in a shading function i.e. an interpolated normal per pixel for
+        top K faces per pixel.
+        """
+        N, H, W, K = 16, 256, 256, 100
+        device = torch.device("cuda:0")
+        color = torch.tensor([1, 1, 1], dtype=torch.float32, device=device)
+        direction = torch.tensor(
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)],
+            dtype=torch.float32,
+            device=device,
+        )
+        normals = torch.tensor([0, 0, 1], dtype=torch.float32, device=device)
+        normals = normals.view(1, 1, 1, 1, 3).expand(N, H, W, K, -1)
+        direction = direction.view(1, 3)
+        color = color.view(1, 3)
+        expected_output = torch.tensor(
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
+            dtype=torch.float32,
+            device=device,
+        )
+        expected_output = expected_output.view(1, 1, 1, 1, 3)
+        expected_output = expected_output.expand(N, H, W, K, -1)
+
+        lights = DirectionalLights(diffuse_color=color, direction=direction)
+        output_light = lights.diffuse(normals=normals)
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+    def test_diffuse_batched_packed(self):
+        """
+        Test with a batch of 2 meshes each of which has faces on a single plane.
+        The normal and light direction are 45 degrees apart for the first mesh
+        and 90 degrees apart for the second mesh.
+
+        The points and normals are in the packed format i.e. no batch dimension.
+        """
+        verts_packed = torch.rand((10, 3))  # points aren't used
+        faces_per_mesh = [6, 4]
+        mesh_to_vert_idx = [0] * faces_per_mesh[0] + [1] * faces_per_mesh[1]
+        mesh_to_vert_idx = torch.tensor(mesh_to_vert_idx, dtype=torch.int64)
+        color = torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.float32)
+        direction = torch.tensor(
+            [
+                [0, 1 / np.sqrt(2), 1 / np.sqrt(2)],
+                [0, 1, 0],  # 90 degrees to normal so zero diffuse light
+            ],
+            dtype=torch.float32,
+        )
+        normals = torch.tensor([[0, 0, 1], [0, 0, 1]], dtype=torch.float32)
+        expected_output = torch.zeros_like(verts_packed, dtype=torch.float32)
+        expected_output[:6, :] += 1 / np.sqrt(2)
+        expected_output[6:, :] = 0.0
+        lights = DirectionalLights(
+            diffuse_color=color[mesh_to_vert_idx, :],
+            direction=direction[mesh_to_vert_idx, :],
+        )
+        output_light = lights.diffuse(normals=normals[mesh_to_vert_idx, :])
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+
+class TestSpecularLighting(unittest.TestCase):
+    def test_specular_directional_lights(self):
+        """
+        Specular highlights depend on the camera position as well as the light
+        position/direction.
+        Test with a single point where:
+        1) the normal and light direction are -45 degrees apart and the normal
+           and camera position are +45 degrees apart. The reflected light ray
+           will be perfectly aligned with the camera so the output is 1.0.
+        2) the normal and light direction are -45 degrees apart and the
+           camera position is behind the point. The output should be zero for
+           this case.
+        """
+        color = torch.tensor([1, 0, 1], dtype=torch.float32)
+        direction = torch.tensor(
+            [-1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        points = torch.tensor([0, 0, 0], dtype=torch.float32)
+        normals = torch.tensor([0, 1, 0], dtype=torch.float32)
+        expected_output = torch.tensor([1.0, 0.0, 1.0], dtype=torch.float32)
+        expected_output = expected_output.view(-1, 1, 3)
+        lights = DirectionalLights(specular_color=color, direction=direction)
+        output_light = lights.specular(
+            points=points[None, None, :],
+            normals=normals[None, None, :],
+            camera_position=camera_position[None, :],
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+        # Change camera position to be behind the point.
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), -1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        expected_output = torch.zeros_like(expected_output)
+        output_light = lights.specular(
+            points=points[None, None, :],
+            normals=normals[None, None, :],
+            camera_position=camera_position[None, :],
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+    def test_specular_point_lights(self):
+        """
+        Replace directional lights with point lights and check the output
+        is the same.
+
+        Test an additional case where the angle between the light reflection
+        direction and the view direction is 30 degrees.
+        """
+        color = torch.tensor([1, 0, 1], dtype=torch.float32)
+        location = torch.tensor([-1, 1, 0], dtype=torch.float32)
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        points = torch.tensor([0, 0, 0], dtype=torch.float32)
+        normals = torch.tensor([0, 1, 0], dtype=torch.float32)
+        expected_output = torch.tensor([1.0, 0.0, 1.0], dtype=torch.float32)
+        expected_output = expected_output.view(-1, 1, 3)
+        lights = PointLights(
+            specular_color=color[None, :], location=location[None, :]
+        )
+        output_light = lights.specular(
+            points=points[None, None, :],
+            normals=normals[None, None, :],
+            camera_position=camera_position[None, :],
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+        # Change camera position to be behind the point
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), -1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        expected_output = torch.zeros_like(expected_output)
+        output_light = lights.specular(
+            points=points[None, None, :],
+            normals=normals[None, None, :],
+            camera_position=camera_position[None, :],
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+        # Change camera direction to be 30 degrees from the reflection direction
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        rotate_30 = RotateAxisAngle(-30, axis="z")
+        camera_position = rotate_30.transform_points(camera_position[None, :])
+        expected_output = torch.tensor(
+            [np.cos(30.0 * np.pi / 180), 0.0, np.cos(30.0 * np.pi / 180)],
+            dtype=torch.float32,
+        )
+        expected_output = expected_output.view(-1, 1, 3)
+        output_light = lights.specular(
+            points=points[None, None, :],
+            normals=normals[None, None, :],
+            camera_position=camera_position[None, :],
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output ** 10))
+
+    def test_specular_batched(self):
+        batch_size = 10
+        color = torch.tensor([1, 0, 1], dtype=torch.float32)
+        direction = torch.tensor(
+            [-1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        points = torch.tensor([0, 0, 0], dtype=torch.float32)
+        normals = torch.tensor([0, 1, 0], dtype=torch.float32)
+        expected_out = torch.tensor([1.0, 0.0, 1.0], dtype=torch.float32)
+
+        # Reshape
+        direction = direction.view(1, 3).expand(batch_size, -1)
+        camera_position = camera_position.view(1, 3).expand(batch_size, -1)
+        normals = normals.view(1, 1, 3).expand(batch_size, -1, -1)
+        points = points.view(1, 1, 3).expand(batch_size, -1, -1)
+        color = color.view(1, 3).expand(batch_size, -1)
+        expected_out = expected_out.view(1, 1, 3).expand(batch_size, 1, 3)
+
+        lights = DirectionalLights(specular_color=color, direction=direction)
+        output_light = lights.specular(
+            points=points,
+            normals=normals,
+            camera_position=camera_position,
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_out))
+
+    def test_specular_batched_broadcast_inputs(self):
+        batch_size = 10
+        color = torch.tensor([1, 0, 1], dtype=torch.float32)
+        direction = torch.tensor(
+            [-1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        points = torch.tensor([0, 0, 0], dtype=torch.float32)
+        normals = torch.tensor([0, 1, 0], dtype=torch.float32)
+        expected_out = torch.tensor([1.0, 0.0, 1.0], dtype=torch.float32)
+
+        # Reshape
+        normals = normals.view(1, 1, 3).expand(batch_size, -1, -1)
+        points = points.view(1, 1, 3).expand(batch_size, -1, -1)
+        expected_out = expected_out.view(1, 1, 3).expand(batch_size, 1, 3)
+
+        # Don't expand the direction, color or camera_position.
+        # These should be broadcasted in the specular function
+        direction = direction.view(1, 3)
+        camera_position = camera_position.view(1, 3)
+        color = color.view(1, 3)
+
+        lights = DirectionalLights(specular_color=color, direction=direction)
+        output_light = lights.specular(
+            points=points,
+            normals=normals,
+            camera_position=camera_position,
+            shininess=torch.tensor(10),
+        )
+        self.assertTrue(torch.allclose(output_light, expected_out))
+
+    def test_specular_batched_arbitrary_input_dims(self):
+        """
+        Test with a batch of inputs where shape of the input is mimicking the
+        shape expected after rasterization i.e. a normal per pixel for
+        top K faces per pixel.
+        """
+        device = torch.device("cuda:0")
+        N, H, W, K = 16, 256, 256, 100
+        color = torch.tensor([1, 0, 1], dtype=torch.float32, device=device)
+        direction = torch.tensor(
+            [-1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        camera_position = torch.tensor(
+            [+1 / np.sqrt(2), 1 / np.sqrt(2), 0], dtype=torch.float32
+        )
+        points = torch.tensor([0, 0, 0], dtype=torch.float32, device=device)
+        normals = torch.tensor([0, 1, 0], dtype=torch.float32, device=device)
+        points = points.view(1, 1, 1, 1, 3).expand(N, H, W, K, 3)
+        normals = normals.view(1, 1, 1, 1, 3).expand(N, H, W, K, 3)
+
+        direction = direction.view(1, 3)
+        color = color.view(1, 3)
+        camera_position = camera_position.view(1, 3)
+
+        expected_output = torch.tensor(
+            [1.0, 0.0, 1.0], dtype=torch.float32, device=device
+        )
+        expected_output = expected_output.view(-1, 1, 1, 1, 3)
+        expected_output = expected_output.expand(N, H, W, K, -1)
+
+        lights = DirectionalLights(specular_color=color, direction=direction)
+        output_light = lights.specular(
+            points=points,
+            normals=normals,
+            camera_position=camera_position,
+            shininess=10.0,
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))
+
+    def test_specular_batched_packed(self):
+        """
+        Test with a batch of 2 meshes each of which has faces on a single plane.
+        The points and normals are in the packed format i.e. no batch dimension.
+        """
+        faces_per_mesh = [6, 4]
+        mesh_to_vert_idx = [0] * faces_per_mesh[0] + [1] * faces_per_mesh[1]
+        mesh_to_vert_idx = torch.tensor(mesh_to_vert_idx, dtype=torch.int64)
+        color = torch.tensor([[1, 1, 1], [1, 0, 1]], dtype=torch.float32)
+        direction = torch.tensor(
+            [[-1 / np.sqrt(2), 1 / np.sqrt(2), 0], [-1, 1, 0]],
+            dtype=torch.float32,
+        )
+        camera_position = torch.tensor(
+            [
+                [+1 / np.sqrt(2), 1 / np.sqrt(2), 0],
+                [+1 / np.sqrt(2), -1 / np.sqrt(2), 0],
+            ],
+            dtype=torch.float32,
+        )
+        points = torch.tensor([[0, 0, 0]], dtype=torch.float32)
+        normals = torch.tensor([[0, 1, 0], [0, 1, 0]], dtype=torch.float32)
+        expected_output = torch.zeros((10, 3), dtype=torch.float32)
+        expected_output[:6, :] += 1.0
+
+        lights = DirectionalLights(
+            specular_color=color[mesh_to_vert_idx, :],
+            direction=direction[mesh_to_vert_idx, :],
+        )
+        output_light = lights.specular(
+            points=points.view(-1, 3).expand(10, -1),
+            normals=normals.view(-1, 3)[mesh_to_vert_idx, :],
+            camera_position=camera_position[mesh_to_vert_idx, :],
+            shininess=10.0,
+        )
+        self.assertTrue(torch.allclose(output_light, expected_output))

tests/test_materials.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import unittest
+import torch
+
+from pytorch3d.renderer.materials import Materials
+
+from common_testing import TestCaseMixin
+
+
+class TestMaterials(TestCaseMixin, unittest.TestCase):
+    def test_init(self):
+        """
+        Initialize Materials class with the default values.
+        """
+        device = torch.device("cuda:0")
+        mat = Materials(device=device)
+        self.assertTrue(torch.is_tensor(mat.ambient_color))
+        self.assertTrue(torch.is_tensor(mat.diffuse_color))
+        self.assertTrue(torch.is_tensor(mat.specular_color))
+        self.assertTrue(torch.is_tensor(mat.shininess))
+        self.assertTrue(mat.ambient_color.device == device)
+        self.assertTrue(mat.diffuse_color.device == device)
+        self.assertTrue(mat.specular_color.device == device)
+        self.assertTrue(mat.shininess.device == device)
+        self.assertTrue(mat.ambient_color.shape == (1, 3))
+        self.assertTrue(mat.diffuse_color.shape == (1, 3))
+        self.assertTrue(mat.specular_color.shape == (1, 3))
+        self.assertTrue(mat.shininess.shape == (1,))
+
+    def test_materials_clone_to(self):
+        device = torch.device("cuda:0")
+        cpu = torch.device("cpu")
+        mat = Materials()
+        new_mat = mat.clone().to(device)
+        self.assertTrue(mat.ambient_color.device == cpu)
+        self.assertTrue(mat.diffuse_color.device == cpu)
+        self.assertTrue(mat.specular_color.device == cpu)
+        self.assertTrue(mat.shininess.device == cpu)
+        self.assertTrue(new_mat.ambient_color.device == device)
+        self.assertTrue(new_mat.diffuse_color.device == device)
+        self.assertTrue(new_mat.specular_color.device == device)
+        self.assertTrue(new_mat.shininess.device == device)
+        self.assertSeparate(new_mat.ambient_color, mat.ambient_color)
+        self.assertSeparate(new_mat.diffuse_color, mat.diffuse_color)
+        self.assertSeparate(new_mat.specular_color, mat.specular_color)
+        self.assertSeparate(new_mat.shininess, mat.shininess)
+
+    def test_initialize_materials_broadcast(self):
+        materials = Materials(
+            ambient_color=torch.randn(10, 3),
+            diffuse_color=torch.randn(1, 3),
+            specular_color=torch.randn(1, 3),
+            shininess=torch.randn(1),
+        )
+        self.assertTrue(materials.ambient_color.shape == (10, 3))
+        self.assertTrue(materials.diffuse_color.shape == (10, 3))
+        self.assertTrue(materials.specular_color.shape == (10, 3))
+        self.assertTrue(materials.shininess.shape == (10,))
+
+    def test_initialize_materials_broadcast_fail(self):
+        """
+        Batch dims have to be the same or 1.
+        """
+        with self.assertRaises(ValueError):
+            Materials(
+                ambient_color=torch.randn(10, 3),
+                diffuse_color=torch.randn(15, 3),
+            )
+
+    def test_initialize_materials_dimensions_fail(self):
+        """
+        Color should have shape (N, 3) or (1, 3), Shininess should have shape
+        (1), (1, 1), (N) or (N, 1)
+        """
+        with self.assertRaises(ValueError):
+            Materials(ambient_color=torch.randn(10, 4))
+
+        with self.assertRaises(ValueError):
+            Materials(shininess=torch.randn(10, 2))
+
+    def test_initialize_materials_mixed_inputs(self):
+        mat = Materials(
+            ambient_color=torch.randn(1, 3), diffuse_color=((1, 1, 1),)
+        )
+        self.assertTrue(mat.ambient_color.shape == (1, 3))
+        self.assertTrue(mat.diffuse_color.shape == (1, 3))
+
+    def test_initialize_materials_mixed_inputs_broadcast(self):
+        mat = Materials(
+            ambient_color=torch.randn(10, 3), diffuse_color=((1, 1, 1),)
+        )
+        self.assertTrue(mat.ambient_color.shape == (10, 3))
+        self.assertTrue(mat.diffuse_color.shape == (10, 3))
+        self.assertTrue(mat.specular_color.shape == (10, 3))
+        self.assertTrue(mat.shininess.shape == (10,))

tests/test_mesh_edge_loss.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import unittest
+import torch
+
+from pytorch3d.loss import mesh_edge_loss
+from pytorch3d.structures import Meshes
+
+from test_sample_points_from_meshes import TestSamplePoints
+
+
+class TestMeshEdgeLoss(unittest.TestCase):
+    def test_empty_meshes(self):
+        device = torch.device("cuda:0")
+        target_length = 0
+        N = 10
+        V = 32
+        verts_list = []
+        faces_list = []
+        for _ in range(N):
+            vn = torch.randint(3, high=V, size=(1,))[0].item()
+            verts = torch.rand((vn, 3), dtype=torch.float32, device=device)
+            faces = torch.tensor([], dtype=torch.int64, device=device)
+            verts_list.append(verts)
+            faces_list.append(faces)
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+        loss = mesh_edge_loss(mesh, target_length=target_length)
+
+        self.assertTrue(
+            torch.allclose(
+                loss, torch.tensor([0.0], dtype=torch.float32, device=device)
+            )
+        )
+        self.assertTrue(loss.requires_grad)
+
+    @staticmethod
+    def mesh_edge_loss_naive(meshes, target_length: float = 0.0):
+        """
+        Naive iterative implementation of mesh loss calculation.
+        """
+        edges_packed = meshes.edges_packed()
+        verts_packed = meshes.verts_packed()
+        edge_to_mesh = meshes.edges_packed_to_mesh_idx()
+        N = len(meshes)
+        device = meshes.device
+        valid = meshes.valid
+        predlosses = torch.zeros((N,), dtype=torch.float32, device=device)
+
+        for b in range(N):
+            if valid[b] == 0:
+                continue
+            mesh_edges = edges_packed[edge_to_mesh == b]
+            verts_edges = verts_packed[mesh_edges]
+            num_edges = mesh_edges.size(0)
+            for e in range(num_edges):
+                v0, v1 = verts_edges[e, 0], verts_edges[e, 1]
+                predlosses[b] += (
+                    (v0 - v1).norm(dim=0, p=2) - target_length
+                ) ** 2.0
+
+            if num_edges > 0:
+                predlosses[b] = predlosses[b] / num_edges
+
+        return predlosses.mean()
+
+    def test_mesh_edge_loss_output(self):
+        """
+        Check outputs of tensorized and iterative implementations are the same.
+        """
+        device = torch.device("cuda:0")
+        target_length = 0.5
+        num_meshes = 10
+        num_verts = 32
+        num_faces = 64
+
+        verts_list = []
+        faces_list = []
+        valid = torch.randint(2, size=(num_meshes,))
+
+        for n in range(num_meshes):
+            if valid[n]:
+                vn = torch.randint(3, high=num_verts, size=(1,))[0].item()
+                fn = torch.randint(vn, high=num_faces, size=(1,))[0].item()
+                verts = torch.rand((vn, 3), dtype=torch.float32, device=device)
+                faces = torch.randint(
+                    vn, size=(fn, 3), dtype=torch.int64, device=device
+                )
+            else:
+                verts = torch.tensor([], dtype=torch.float32, device=device)
+                faces = torch.tensor([], dtype=torch.int64, device=device)
+            verts_list.append(verts)
+            faces_list.append(faces)
+        meshes = Meshes(verts=verts_list, faces=faces_list)
+        loss = mesh_edge_loss(meshes, target_length=target_length)
+
+        predloss = TestMeshEdgeLoss.mesh_edge_loss_naive(meshes, target_length)
+        self.assertTrue(torch.allclose(loss, predloss))
+
+    @staticmethod
+    def mesh_edge_loss(
+        num_meshes: int = 10, max_v: int = 100, max_f: int = 300
+    ):
+        meshes = TestSamplePoints.init_meshes(
+            num_meshes, max_v, max_f, device="cuda:0"
+        )
+        torch.cuda.synchronize()
+
+        def compute_loss():
+            mesh_edge_loss(meshes, target_length=0.0)
+            torch.cuda.synchronize()
+
+        return compute_loss

tests/test_mesh_laplacian_smoothing.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+
+import unittest
+import torch
+
+from pytorch3d.loss.mesh_laplacian_smoothing import mesh_laplacian_smoothing
+from pytorch3d.structures.meshes import Meshes
+
+
+class TestLaplacianSmoothing(unittest.TestCase):
+    @staticmethod
+    def laplacian_smoothing_naive_uniform(meshes):
+        """
+        Naive implementation of laplacian smoothing with uniform weights.
+        """
+        verts_packed = meshes.verts_packed()  # (sum(V_n), 3)
+        faces_packed = meshes.faces_packed()  # (sum(F_n), 3)
+        V = verts_packed.shape[0]
+
+        L = torch.zeros((V, V), dtype=torch.float32, device=meshes.device)
+
+        # filling L with the face pairs should be the same as edge pairs
+        for f in faces_packed:
+            L[f[0], f[1]] = 1
+            L[f[0], f[2]] = 1
+            L[f[1], f[2]] = 1
+            # symetric
+            L[f[1], f[0]] = 1
+            L[f[2], f[0]] = 1
+            L[f[2], f[1]] = 1
+
+        norm_w = L.sum(dim=1, keepdims=True)
+        idx = norm_w > 0
+        norm_w[idx] = 1.0 / norm_w[idx]
+
+        loss = (L.mm(verts_packed) * norm_w - verts_packed).norm(dim=1)
+
+        weights = torch.zeros(V, dtype=torch.float32, device=meshes.device)
+        for v in range(V):
+            weights[v] = meshes.num_verts_per_mesh()[
+                meshes.verts_packed_to_mesh_idx()[v]
+            ]
+        weights = 1.0 / weights
+        loss = loss * weights
+
+        return loss.sum() / len(meshes)
+
+    @staticmethod
+    def laplacian_smoothing_naive_cot(meshes, method: str = "cot"):
+        """
+        Naive implementation of laplacian smoothing wit cotangent weights.
+        """
+        verts_packed = meshes.verts_packed()  # (sum(V_n), 3)
+        faces_packed = meshes.faces_packed()  # (sum(F_n), 3)
+        V = verts_packed.shape[0]
+
+        L = torch.zeros((V, V), dtype=torch.float32, device=meshes.device)
+        inv_areas = torch.zeros(
+            (V, 1), dtype=torch.float32, device=meshes.device
+        )
+
+        for f in faces_packed:
+            v0 = verts_packed[f[0], :]
+            v1 = verts_packed[f[1], :]
+            v2 = verts_packed[f[2], :]
+            A = (v1 - v2).norm()
+            B = (v0 - v2).norm()
+            C = (v0 - v1).norm()
+            s = 0.5 * (A + B + C)
+
+            face_area = (
+                (s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
+            )
+            inv_areas[f[0]] += face_area
+            inv_areas[f[1]] += face_area
+            inv_areas[f[2]] += face_area
+
+            A2, B2, C2 = A * A, B * B, C * C
+            cota = (B2 + C2 - A2) / face_area / 4.0
+            cotb = (A2 + C2 - B2) / face_area / 4.0
+            cotc = (A2 + B2 - C2) / face_area / 4.0
+
+            L[f[1], f[2]] += cota
+            L[f[2], f[0]] += cotb
+            L[f[0], f[1]] += cotc
+            # symetric
+            L[f[2], f[1]] += cota
+            L[f[0], f[2]] += cotb
+            L[f[1], f[0]] += cotc
+
+        idx = inv_areas > 0
+        inv_areas[idx] = 1.0 / inv_areas[idx]
+
+        norm_w = L.sum(dim=1, keepdims=True)
+        idx = norm_w > 0
+        norm_w[idx] = 1.0 / norm_w[idx]
+
+        if method == "cotcurv":
+            loss = (L.mm(verts_packed) - verts_packed) * inv_areas * 0.25
+            loss = loss.norm(dim=1)
+        else:
+            loss = L.mm(verts_packed) * norm_w - verts_packed
+            loss = loss.norm(dim=1)
+
+        weights = torch.zeros(V, dtype=torch.float32, device=meshes.device)
+        for v in range(V):
+            weights[v] = meshes.num_verts_per_mesh()[
+                meshes.verts_packed_to_mesh_idx()[v]
+            ]
+        weights = 1.0 / weights
+        loss = loss * weights
+
+        return loss.sum() / len(meshes)
+
+    @staticmethod
+    def init_meshes(
+        num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
+    ):
+        device = torch.device("cuda:0")
+        verts_list = []
+        faces_list = []
+        for _ in range(num_meshes):
+            verts = (
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device)
+                * 2.0
+                - 1.0
+            )  # verts in the space of [-1, 1]
+            faces = torch.stack(
+                [
+                    torch.randperm(num_verts, device=device)[:3]
+                    for _ in range(num_faces)
+                ],
+                dim=0,
+            )
+            # avoids duplicate vertices in a face
+            verts_list.append(verts)
+            faces_list.append(faces)
+        meshes = Meshes(verts_list, faces_list)
+
+        return meshes
+
+    def test_laplacian_smoothing_uniform(self):
+        """
+        Test Laplacian Smoothing with uniform weights.
+        """
+        meshes = TestLaplacianSmoothing.init_meshes(10, 100, 300)
+
+        # feats in list
+        out = mesh_laplacian_smoothing(meshes, method="uniform")
+        naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_uniform(
+            meshes
+        )
+
+        self.assertTrue(torch.allclose(out, naive_out))
+
+    def test_laplacian_smoothing_cot(self):
+        """
+        Test Laplacian Smoothing with uniform weights.
+        """
+        meshes = TestLaplacianSmoothing.init_meshes(10, 100, 300)
+
+        # feats in list
+        out = mesh_laplacian_smoothing(meshes, method="cot")
+        naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_cot(
+            meshes, method="cot"
+        )
+
+        self.assertTrue(torch.allclose(out, naive_out))
+
+    def test_laplacian_smoothing_cotcurv(self):
+        """
+        Test Laplacian Smoothing with uniform weights.
+        """
+        meshes = TestLaplacianSmoothing.init_meshes(10, 100, 300)
+
+        # feats in list
+        out = mesh_laplacian_smoothing(meshes, method="cotcurv")
+        naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_cot(
+            meshes, method="cotcurv"
+        )
+
+        self.assertTrue(torch.allclose(out, naive_out))
+
+    @staticmethod
+    def laplacian_smoothing_with_init(
+        num_meshes: int, num_verts: int, num_faces: int, device: str = "cpu"
+    ):
+        device = torch.device(device)
+        verts_list = []
+        faces_list = []
+        for _ in range(num_meshes):
+            verts = torch.rand(
+                (num_verts, 3), dtype=torch.float32, device=device
+            )
+            faces = torch.randint(
+                num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
+            )
+            verts_list.append(verts)
+            faces_list.append(faces)
+        meshes = Meshes(verts_list, faces_list)
+        torch.cuda.synchronize()
+
+        def smooth():
+            mesh_laplacian_smoothing(meshes, method="cotcurv")
+            torch.cuda.synchronize()
+
+        return smooth

tests/test_mesh_normal_consistency.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+
+import unittest
+import torch
+
+from pytorch3d.loss.mesh_normal_consistency import mesh_normal_consistency
+from pytorch3d.structures.meshes import Meshes
+from pytorch3d.utils.ico_sphere import ico_sphere
+
+
+class TestMeshNormalConsistency(unittest.TestCase):
+    @staticmethod
+    def init_faces(num_verts: int = 1000):
+        faces = []
+        for f0 in range(num_verts):
+            for f1 in range(f0 + 1, num_verts):
+                f2 = torch.arange(f1 + 1, num_verts)
+                n = f2.shape[0]
+                if n == 0:
+                    continue
+                faces.append(
+                    torch.stack(
+                        [
+                            torch.full((n,), f0, dtype=torch.int64),
+                            torch.full((n,), f1, dtype=torch.int64),
+                            f2,
+                        ],
+                        dim=1,
+                    )
+                )
+        faces = torch.cat(faces, 0)
+        return faces
+
+    @staticmethod
+    def init_meshes(
+        num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
+    ):
+        device = torch.device("cuda:0")
+        valid_faces = TestMeshNormalConsistency.init_faces(num_verts).to(device)
+        verts_list = []
+        faces_list = []
+        for _ in range(num_meshes):
+            verts = (
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device)
+                * 2.0
+                - 1.0
+            )  # verts in the space of [-1, 1]
+            """
+            faces = torch.stack(
+                [
+                    torch.randperm(num_verts, device=device)[:3]
+                    for _ in range(num_faces)
+                ],
+                dim=0,
+            )
+            # avoids duplicate vertices in a face
+            """
+            idx = torch.randperm(valid_faces.shape[0], device=device)[
+                : min(valid_faces.shape[0], num_faces)
+            ]
+            faces = valid_faces[idx]
+            verts_list.append(verts)
+            faces_list.append(faces)
+        meshes = Meshes(verts_list, faces_list)
+        return meshes
+
+    @staticmethod
+    def mesh_normal_consistency_naive(meshes):
+        """
+        Naive iterative implementation of mesh normal consistency.
+        """
+        N = len(meshes)
+        verts_packed = meshes.verts_packed()
+        faces_packed = meshes.faces_packed()
+        edges_packed = meshes.edges_packed()
+        face_to_edge = meshes.faces_packed_to_edges_packed()
+        edges_packed_to_mesh_idx = meshes.edges_packed_to_mesh_idx()
+
+        E = edges_packed.shape[0]
+        loss = []
+        mesh_idx = []
+
+        for e in range(E):
+            face_idx = face_to_edge.eq(e).any(1).nonzero()  # indexed to faces
+            v0 = verts_packed[edges_packed[e, 0]]
+            v1 = verts_packed[edges_packed[e, 1]]
+            normals = []
+            for f in face_idx:
+                v2 = -1
+                for j in range(3):
+                    if (
+                        faces_packed[f, j] != edges_packed[e, 0]
+                        and faces_packed[f, j] != edges_packed[e, 1]
+                    ):
+                        v2 = faces_packed[f, j]
+                assert v2 > -1
+                v2 = verts_packed[v2]
+                normals.append((v1 - v0).view(-1).cross((v2 - v0).view(-1)))
+            for i in range(len(normals) - 1):
+                for j in range(1, len(normals)):
+                    if i != j:
+                        mesh_idx.append(edges_packed_to_mesh_idx[e])
+                        loss.append(
+                            (
+                                1
+                                - torch.cosine_similarity(
+                                    normals[i].view(1, 3),
+                                    -normals[j].view(1, 3),
+                                )
+                            )
+                        )
+
+        mesh_idx = torch.tensor(mesh_idx, device=meshes.device)
+        num = mesh_idx.bincount(minlength=N)
+        weights = 1.0 / num[mesh_idx].float()
+
+        loss = torch.cat(loss) * weights
+        return loss.sum() / N
+
+    def test_mesh_normal_consistency_simple(self):
+        r"""
+        Mesh 1:
+                        v3
+                        /\
+                       /  \
+                   e4 / f1 \ e3
+                     /      \
+                 v2 /___e2___\ v1
+                    \        /
+                     \      /
+                 e1   \ f0 / e0
+                       \  /
+                        \/
+                        v0
+        """
+        device = torch.device("cuda:0")
+        # mesh1 shown above
+        verts1 = torch.rand((4, 3), dtype=torch.float32, device=device)
+        faces1 = torch.tensor(
+            [[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device
+        )
+
+        # mesh2 is a cuboid with 8 verts, 12 faces and 18 edges
+        verts2 = torch.tensor(
+            [
+                [0, 0, 0],
+                [0, 0, 1],
+                [0, 1, 0],
+                [0, 1, 1],
+                [1, 0, 0],
+                [1, 0, 1],
+                [1, 1, 0],
+                [1, 1, 1],
+            ],
+            dtype=torch.float32,
+            device=device,
+        )
+        faces2 = torch.tensor(
+            [
+                [0, 1, 2],
+                [1, 3, 2],  # left face: 0, 1
+                [2, 3, 6],
+                [3, 7, 6],  # bottom face: 2, 3
+                [0, 2, 6],
+                [0, 6, 4],  # front face: 4, 5
+                [0, 5, 1],
+                [0, 4, 5],  # up face: 6, 7
+                [6, 7, 5],
+                [6, 5, 4],  # right face: 8, 9
+                [1, 7, 3],
+                [1, 5, 7],  # back face: 10, 11
+            ],
+            dtype=torch.int64,
+            device=device,
+        )
+
+        # mesh3 is like mesh1 but with another face added to e2
+        verts3 = torch.rand((5, 3), dtype=torch.float32, device=device)
+        faces3 = torch.tensor(
+            [[0, 1, 2], [2, 1, 3], [2, 1, 4]], dtype=torch.int64, device=device
+        )
+
+        meshes = Meshes(
+            verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3]
+        )
+
+        # mesh1: normal consistency computation
+        n0 = (verts1[1] - verts1[2]).cross(verts1[3] - verts1[2])
+        n1 = (verts1[1] - verts1[2]).cross(verts1[0] - verts1[2])
+        loss1 = 1.0 - torch.cosine_similarity(n0.view(1, 3), -n1.view(1, 3))
+
+        # mesh2: normal consistency computation
+        # In the cube mesh, 6 edges are shared with coplanar faces (loss=0),
+        # 12 edges are shared by perpendicular faces (loss=1)
+        loss2 = 12.0 / 18
+
+        # mesh3
+        n0 = (verts3[1] - verts3[2]).cross(verts3[3] - verts3[2])
+        n1 = (verts3[1] - verts3[2]).cross(verts3[0] - verts3[2])
+        n2 = (verts3[1] - verts3[2]).cross(verts3[4] - verts3[2])
+        loss3 = (
+            3.0
+            - torch.cosine_similarity(n0.view(1, 3), -n1.view(1, 3))
+            - torch.cosine_similarity(n0.view(1, 3), -n2.view(1, 3))
+            - torch.cosine_similarity(n1.view(1, 3), -n2.view(1, 3))
+        )
+        loss3 /= 3.0
+
+        loss = (loss1 + loss2 + loss3) / 3.0
+
+        out = mesh_normal_consistency(meshes)
+
+        self.assertTrue(torch.allclose(out, loss))
+
+    def test_mesh_normal_consistency(self):
+        """
+        Test Mesh Normal Consistency for random meshes.
+        """
+        meshes = TestMeshNormalConsistency.init_meshes(5, 100, 300)
+
+        out1 = mesh_normal_consistency(meshes)
+        out2 = TestMeshNormalConsistency.mesh_normal_consistency_naive(meshes)
+
+        self.assertTrue(torch.allclose(out1, out2))
+
+    @staticmethod
+    def mesh_normal_consistency_with_ico(
+        num_meshes: int, level: int = 3, device: str = "cpu"
+    ):
+        device = torch.device(device)
+        mesh = ico_sphere(level, device)
+        verts, faces = mesh.get_mesh_verts_faces(0)
+        verts_list = [verts.clone() for _ in range(num_meshes)]
+        faces_list = [faces.clone() for _ in range(num_meshes)]
+        meshes = Meshes(verts_list, faces_list)
+        torch.cuda.synchronize()
+
+        def loss():
+            mesh_normal_consistency(meshes)
+            torch.cuda.synchronize()
+
+        return loss

tests/test_meshes.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import numpy as np
+import unittest
+import torch
+
+from pytorch3d.structures.meshes import Meshes
+
+from common_testing import TestCaseMixin
+
+
+class TestMeshes(TestCaseMixin, unittest.TestCase):
+    def setUp(self) -> None:
+        np.random.seed(42)
+        torch.manual_seed(42)
+
+    @staticmethod
+    def init_mesh(
+        num_meshes: int = 10,
+        max_v: int = 100,
+        max_f: int = 300,
+        lists_to_tensors: bool = False,
+        device: str = "cpu",
+    ):
+        """
+        Function to generate a Meshes object of N meshes with
+        random numbers of vertices and faces.
+
+        Args:
+            num_meshes: Number of meshes to generate.
+            max_v: Max number of vertices per mesh.
+            max_f: Max number of faces per mesh.
+            lists_to_tensors: Determines whether the generated meshes should be
+                              constructed from lists (=False) or
+                              a tensor (=True) of faces/verts.
+
+        Returns:
+            Meshes object.
+        """
+        device = torch.device(device)
+
+        verts_list = []
+        faces_list = []
+
+        # Randomly generate numbers of faces and vertices in each mesh.
+        if lists_to_tensors:
+            # If we define faces/verts with tensors, f/v has to be the
+            # same for each mesh in the batch.
+            f = torch.randint(max_f, size=(1,), dtype=torch.int32)
+            v = torch.randint(3, high=max_v, size=(1,), dtype=torch.int32)
+            f = f.repeat(num_meshes)
+            v = v.repeat(num_meshes)
+        else:
+            # For lists of faces and vertices, we can sample different v/f
+            # per mesh.
+            f = torch.randint(max_f, size=(num_meshes,), dtype=torch.int32)
+            v = torch.randint(
+                3, high=max_v, size=(num_meshes,), dtype=torch.int32
+            )
+
+        # Generate the actual vertices and faces.
+        for i in range(num_meshes):
+            verts = torch.rand((v[i], 3), dtype=torch.float32, device=device)
+            faces = torch.randint(
+                v[i], size=(f[i], 3), dtype=torch.int64, device=device
+            )
+            verts_list.append(verts)
+            faces_list.append(faces)
+
+        if lists_to_tensors:
+            verts_list = torch.stack(verts_list)
+            faces_list = torch.stack(faces_list)
+
+        return Meshes(verts=verts_list, faces=faces_list)
+
+    @staticmethod
+    def init_simple_mesh(device: str = "cpu"):
+        """
+        Returns a Meshes data structure of simple mesh examples.
+
+        Returns:
+            Meshes object.
+        """
+        device = torch.device(device)
+
+        verts = [
+            torch.tensor(
+                [[0.1, 0.3, 0.5], [0.5, 0.2, 0.1], [0.6, 0.8, 0.7]],
+                dtype=torch.float32,
+                device=device,
+            ),
+            torch.tensor(
+                [
+                    [0.1, 0.3, 0.3],
+                    [0.6, 0.7, 0.8],
+                    [0.2, 0.3, 0.4],
+                    [0.1, 0.5, 0.3],
+                ],
+                dtype=torch.float32,
+                device=device,
+            ),
+            torch.tensor(
+                [
+                    [0.7, 0.3, 0.6],
+                    [0.2, 0.4, 0.8],
+                    [0.9, 0.5, 0.2],
+                    [0.2, 0.3, 0.4],
+                    [0.9, 0.3, 0.8],
+                ],
+                dtype=torch.float32,
+                device=device,
+            ),
+        ]
+        faces = [
+            torch.tensor([[0, 1, 2]], dtype=torch.int64, device=device),
+            torch.tensor(
+                [[0, 1, 2], [1, 2, 3]], dtype=torch.int64, device=device
+            ),
+            torch.tensor(
+                [
+                    [1, 2, 0],
+                    [0, 1, 3],
+                    [2, 3, 1],
+                    [4, 3, 2],
+                    [4, 0, 1],
+                    [4, 3, 1],
+                    [4, 2, 1],
+                ],
+                dtype=torch.int64,
+                device=device,
+            ),
+        ]
+        return Meshes(verts=verts, faces=faces)
+
+    def test_simple(self):
+        mesh = TestMeshes.init_simple_mesh("cuda:0")
+
+        self.assertClose(
+            mesh.verts_packed_to_mesh_idx().cpu(),
+            torch.tensor([0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2]),
+        )
+        self.assertClose(
+            mesh.mesh_to_verts_packed_first_idx().cpu(), torch.tensor([0, 3, 7])
+        )
+        self.assertClose(
+            mesh.num_verts_per_mesh().cpu(), torch.tensor([3, 4, 5])
+        )
+        self.assertClose(
+            mesh.verts_padded_to_packed_idx().cpu(),
+            torch.tensor([0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 13, 14]),
+        )
+        self.assertClose(
+            mesh.faces_packed_to_mesh_idx().cpu(),
+            torch.tensor([0, 1, 1, 2, 2, 2, 2, 2, 2, 2]),
+        )
+        self.assertClose(
+            mesh.mesh_to_faces_packed_first_idx().cpu(), torch.tensor([0, 1, 3])
+        )
+        self.assertClose(
+            mesh.num_faces_per_mesh().cpu(), torch.tensor([1, 2, 7])
+        )
+        self.assertClose(
+            mesh.num_edges_per_mesh().cpu(),
+            torch.tensor([3, 5, 10], dtype=torch.int32),
+        )
+
+    def test_simple_random_meshes(self):
+
+        # Define the test mesh object either as a list or tensor of faces/verts.
+        for lists_to_tensors in (False, True):
+            N = 10
+            mesh = TestMeshes.init_mesh(
+                N, 100, 300, lists_to_tensors=lists_to_tensors
+            )
+            verts_list = mesh.verts_list()
+            faces_list = mesh.faces_list()
+
+            # Check batch calculations.
+            verts_padded = mesh.verts_padded()
+            faces_padded = mesh.faces_padded()
+            verts_per_mesh = mesh.num_verts_per_mesh()
+            faces_per_mesh = mesh.num_faces_per_mesh()
+            for n in range(N):
+                v = verts_list[n].shape[0]
+                f = faces_list[n].shape[0]
+                self.assertClose(verts_padded[n, :v, :], verts_list[n])
+                if verts_padded.shape[1] > v:
+                    self.assertTrue(verts_padded[n, v:, :].eq(0).all())
+                self.assertClose(faces_padded[n, :f, :], faces_list[n])
+                if faces_padded.shape[1] > f:
+                    self.assertTrue(faces_padded[n, f:, :].eq(-1).all())
+                self.assertEqual(verts_per_mesh[n], v)
+                self.assertEqual(faces_per_mesh[n], f)
+
+            # Check compute packed.
+            verts_packed = mesh.verts_packed()
+            vert_to_mesh = mesh.verts_packed_to_mesh_idx()
+            mesh_to_vert = mesh.mesh_to_verts_packed_first_idx()
+            faces_packed = mesh.faces_packed()
+            face_to_mesh = mesh.faces_packed_to_mesh_idx()
+            mesh_to_face = mesh.mesh_to_faces_packed_first_idx()
+
+            curv, curf = 0, 0
+            for n in range(N):
+                v = verts_list[n].shape[0]
+                f = faces_list[n].shape[0]
+                self.assertClose(
+                    verts_packed[curv : curv + v, :], verts_list[n]
+                )
+                self.assertClose(
+                    faces_packed[curf : curf + f, :] - curv, faces_list[n]
+                )
+                self.assertTrue(vert_to_mesh[curv : curv + v].eq(n).all())
+                self.assertTrue(face_to_mesh[curf : curf + f].eq(n).all())
+                self.assertTrue(mesh_to_vert[n] == curv)
+                self.assertTrue(mesh_to_face[n] == curf)
+                curv += v
+                curf += f
+
+            # Check compute edges and compare with numpy unique.
+            edges = mesh.edges_packed().cpu().numpy()
+            edge_to_mesh_idx = mesh.edges_packed_to_mesh_idx().cpu().numpy()
+            num_edges_per_mesh = mesh.num_edges_per_mesh().cpu().numpy()
+
+            npfaces_packed = mesh.faces_packed().cpu().numpy()
+            e01 = npfaces_packed[:, [0, 1]]
+            e12 = npfaces_packed[:, [1, 2]]
+            e20 = npfaces_packed[:, [2, 0]]
+            npedges = np.concatenate((e12, e20, e01), axis=0)
+            npedges = np.sort(npedges, axis=1)
+
+            unique_edges, unique_idx = np.unique(
+                npedges, return_index=True, axis=0
+            )
+            self.assertTrue(np.allclose(edges, unique_edges))
+            temp = face_to_mesh.cpu().numpy()
+            temp = np.concatenate((temp, temp, temp), axis=0)
+            edge_to_mesh = temp[unique_idx]
+            self.assertTrue(np.allclose(edge_to_mesh_idx, edge_to_mesh))
+            num_edges = np.bincount(edge_to_mesh, minlength=N)
+            self.assertTrue(np.allclose(num_edges_per_mesh, num_edges))
+
+    def test_allempty(self):
+        verts_list = []
+        faces_list = []
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+        self.assertEqual(len(mesh), 0)
+        self.assertEqual(mesh.verts_padded().shape[0], 0)
+        self.assertEqual(mesh.faces_padded().shape[0], 0)
+        self.assertEqual(mesh.verts_packed().shape[0], 0)
+        self.assertEqual(mesh.faces_packed().shape[0], 0)
+
+    def test_empty(self):
+        N, V, F = 10, 100, 300
+        device = torch.device("cuda:0")
+        verts_list = []
+        faces_list = []
+        valid = torch.randint(2, size=(N,), dtype=torch.uint8, device=device)
+        for n in range(N):
+            if valid[n]:
+                v = torch.randint(
+                    3, high=V, size=(1,), dtype=torch.int32, device=device
+                )[0]
+                f = torch.randint(
+                    F, size=(1,), dtype=torch.int32, device=device
+                )[0]
+                verts = torch.rand((v, 3), dtype=torch.float32, device=device)
+                faces = torch.randint(
+                    v, size=(f, 3), dtype=torch.int64, device=device
+                )
+            else:
+                verts = torch.tensor([], dtype=torch.float32, device=device)
+                faces = torch.tensor([], dtype=torch.int64, device=device)
+            verts_list.append(verts)
+            faces_list.append(faces)
+
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+        verts_padded = mesh.verts_padded()
+        faces_padded = mesh.faces_padded()
+        verts_per_mesh = mesh.num_verts_per_mesh()
+        faces_per_mesh = mesh.num_faces_per_mesh()
+        for n in range(N):
+            v = len(verts_list[n])
+            f = len(faces_list[n])
+            if v > 0:
+                self.assertClose(verts_padded[n, :v, :], verts_list[n])
+                if verts_padded.shape[1] > v:
+                    self.assertTrue(verts_padded[n, v:, :].eq(0).all())
+            if f > 0:
+                self.assertClose(faces_padded[n, :f, :], faces_list[n])
+                if faces_padded.shape[1] > f:
+                    self.assertTrue(faces_padded[n, f:, :].eq(-1).all())
+            self.assertTrue(verts_per_mesh[n] == v)
+            self.assertTrue(faces_per_mesh[n] == f)
+
+    def test_padding(self):
+        N, V, F = 10, 100, 300
+        device = torch.device("cuda:0")
+        verts, faces = [], []
+        valid = torch.randint(2, size=(N,), dtype=torch.uint8, device=device)
+        num_verts, num_faces = (
+            torch.zeros(N, dtype=torch.int32),
+            torch.zeros(N, dtype=torch.int32),
+        )
+        for n in range(N):
+            verts.append(torch.rand((V, 3), dtype=torch.float32, device=device))
+            this_faces = torch.full(
+                (F, 3), -1, dtype=torch.int64, device=device
+            )
+            if valid[n]:
+                v = torch.randint(
+                    3, high=V, size=(1,), dtype=torch.int32, device=device
+                )[0]
+                f = torch.randint(
+                    F, size=(1,), dtype=torch.int32, device=device
+                )[0]
+                this_faces[:f, :] = torch.randint(
+                    v, size=(f, 3), dtype=torch.int64, device=device
+                )
+                num_verts[n] = v
+                num_faces[n] = f
+            faces.append(this_faces)
+
+        mesh = Meshes(verts=torch.stack(verts), faces=torch.stack(faces))
+
+        self.assertListEqual(
+            mesh.num_faces_per_mesh().tolist(), num_faces.tolist()
+        )
+
+        for n, (vv, ff) in enumerate(zip(mesh.verts_list(), mesh.faces_list())):
+            self.assertClose(ff, faces[n][: num_faces[n]])
+            self.assertClose(vv, verts[n])
+
+        new_faces = [ff.clone() for ff in faces]
+        v = torch.randint(
+            3, high=V, size=(1,), dtype=torch.int32, device=device
+        )[0]
+        f = torch.randint(F - 10, size=(1,), dtype=torch.int32, device=device)[
+            0
+        ]
+        this_faces = torch.full((F, 3), -1, dtype=torch.int64, device=device)
+        this_faces[10 : f + 10, :] = torch.randint(
+            v, size=(f, 3), dtype=torch.int64, device=device
+        )
+        new_faces[3] = this_faces
+
+        with self.assertRaisesRegex(ValueError, "Padding of faces"):
+            Meshes(verts=torch.stack(verts), faces=torch.stack(new_faces))
+
+    def test_clone(self):
+        N = 5
+        mesh = TestMeshes.init_mesh(N, 10, 100)
+        for force in [0, 1]:
+            if force:
+                # force mesh to have computed attributes
+                mesh.verts_packed()
+                mesh.edges_packed()
+                mesh.verts_padded()
+
+            new_mesh = mesh.clone()
+
+            # Modify tensors in both meshes.
+            new_mesh._verts_list[0] = new_mesh._verts_list[0] * 5
+            mesh._num_verts_per_mesh = torch.randint_like(
+                mesh.num_verts_per_mesh(), high=10
+            )
+
+            # Check cloned and original Meshes objects do not share tensors.
+            self.assertFalse(
+                torch.allclose(new_mesh._verts_list[0], mesh._verts_list[0])
+            )
+            self.assertFalse(
+                torch.allclose(
+                    mesh.num_verts_per_mesh(), new_mesh.num_verts_per_mesh()
+                )
+            )
+            self.assertSeparate(new_mesh.verts_packed(), mesh.verts_packed())
+            self.assertSeparate(new_mesh.verts_padded(), mesh.verts_padded())
+            self.assertSeparate(new_mesh.faces_packed(), mesh.faces_packed())
+            self.assertSeparate(new_mesh.faces_padded(), mesh.faces_padded())
+            self.assertSeparate(new_mesh.edges_packed(), mesh.edges_packed())
+
+    def test_laplacian_packed(self):
+        def naive_laplacian_packed(meshes):
+            verts_packed = meshes.verts_packed()
+            edges_packed = meshes.edges_packed()
+            V = verts_packed.shape[0]
+
+            L = torch.zeros((V, V), dtype=torch.float32, device=meshes.device)
+            for e in edges_packed:
+                L[e[0], e[1]] = 1
+                # symetric
+                L[e[1], e[0]] = 1
+
+            deg = L.sum(1).view(-1, 1)
+            deg[deg > 0] = 1.0 / deg[deg > 0]
+            L = L * deg
+            diag = torch.eye(V, dtype=torch.float32, device=meshes.device)
+            L.masked_fill_(diag > 0, -1)
+            return L
+
+        # Note that we don't test with random meshes for this case, as the
+        # definition of Laplacian is defined for simple graphs (aka valid meshes)
+        meshes = TestMeshes.init_simple_mesh("cuda:0")
+
+        lapl_naive = naive_laplacian_packed(meshes)
+        lapl = meshes.laplacian_packed().to_dense()
+        # check with naive
+        self.assertClose(lapl, lapl_naive)
+
+    def test_offset_verts(self):
+        def naive_offset_verts(mesh, vert_offsets_packed):
+            # new Meshes class
+            new_verts_packed = mesh.verts_packed() + vert_offsets_packed
+            new_verts_list = list(
+                new_verts_packed.split(mesh.num_verts_per_mesh().tolist(), 0)
+            )
+            new_faces_list = [f.clone() for f in mesh.faces_list()]
+            return Meshes(verts=new_verts_list, faces=new_faces_list)
+
+        N = 5
+        mesh = TestMeshes.init_mesh(N, 10, 100)
+        all_v = mesh.verts_packed().size(0)
+        verts_per_mesh = mesh.num_verts_per_mesh()
+        for force in [0, 1]:
+            if force:
+                # force mesh to have computed attributes
+                mesh._compute_packed(refresh=True)
+                mesh._compute_padded()
+                mesh._compute_edges_packed()
+                mesh.verts_padded_to_packed_idx()
+                mesh._compute_face_areas_normals(refresh=True)
+                mesh._compute_vertex_normals(refresh=True)
+
+            deform = torch.rand(
+                (all_v, 3), dtype=torch.float32, device=mesh.device
+            )
+            # new meshes class to hold the deformed mesh
+            new_mesh_naive = naive_offset_verts(mesh, deform)
+
+            new_mesh = mesh.offset_verts(deform)
+
+            # check verts_list & faces_list
+            verts_cumsum = torch.cumsum(verts_per_mesh, 0).tolist()
+            verts_cumsum.insert(0, 0)
+            for i in range(N):
+                self.assertClose(
+                    new_mesh.verts_list()[i],
+                    mesh.verts_list()[i]
+                    + deform[verts_cumsum[i] : verts_cumsum[i + 1]],
+                )
+                self.assertClose(
+                    new_mesh.verts_list()[i], new_mesh_naive.verts_list()[i]
+                )
+                self.assertClose(
+                    mesh.faces_list()[i], new_mesh_naive.faces_list()[i]
+                )
+                self.assertClose(
+                    new_mesh.faces_list()[i], new_mesh_naive.faces_list()[i]
+                )
+                # check faces and vertex normals
+                self.assertClose(
+                    new_mesh.verts_normals_list()[i],
+                    new_mesh_naive.verts_normals_list()[i],
+                )
+                self.assertClose(
+                    new_mesh.faces_normals_list()[i],
+                    new_mesh_naive.faces_normals_list()[i],
+                )
+
+            # check padded & packed
+            self.assertClose(
+                new_mesh.faces_padded(), new_mesh_naive.faces_padded()
+            )
+            self.assertClose(
+                new_mesh.verts_padded(), new_mesh_naive.verts_padded()
+            )
+            self.assertClose(
+                new_mesh.faces_packed(), new_mesh_naive.faces_packed()
+            )
+            self.assertClose(
+                new_mesh.verts_packed(), new_mesh_naive.verts_packed()
+            )
+            self.assertClose(
+                new_mesh.edges_packed(), new_mesh_naive.edges_packed()
+            )
+            self.assertClose(
+                new_mesh.verts_packed_to_mesh_idx(),
+                new_mesh_naive.verts_packed_to_mesh_idx(),
+            )
+            self.assertClose(
+                new_mesh.mesh_to_verts_packed_first_idx(),
+                new_mesh_naive.mesh_to_verts_packed_first_idx(),
+            )
+            self.assertClose(
+                new_mesh.num_verts_per_mesh(),
+                new_mesh_naive.num_verts_per_mesh(),
+            )
+            self.assertClose(
+                new_mesh.faces_packed_to_mesh_idx(),
+                new_mesh_naive.faces_packed_to_mesh_idx(),
+            )
+            self.assertClose(
+                new_mesh.mesh_to_faces_packed_first_idx(),
+                new_mesh_naive.mesh_to_faces_packed_first_idx(),
+            )
+            self.assertClose(
+                new_mesh.num_faces_per_mesh(),
+                new_mesh_naive.num_faces_per_mesh(),
+            )
+            self.assertClose(
+                new_mesh.edges_packed_to_mesh_idx(),
+                new_mesh_naive.edges_packed_to_mesh_idx(),
+            )
+            self.assertClose(
+                new_mesh.verts_padded_to_packed_idx(),
+                new_mesh_naive.verts_padded_to_packed_idx(),
+            )
+            self.assertTrue(all(new_mesh.valid == new_mesh_naive.valid))
+            self.assertTrue(new_mesh.equisized == new_mesh_naive.equisized)
+
+            # check face areas, normals and vertex normals
+            self.assertClose(
+                new_mesh.verts_normals_packed(),
+                new_mesh_naive.verts_normals_packed(),
+            )
+            self.assertClose(
+                new_mesh.verts_normals_padded(),
+                new_mesh_naive.verts_normals_padded(),
+            )
+            self.assertClose(
+                new_mesh.faces_normals_packed(),
+                new_mesh_naive.faces_normals_packed(),
+            )
+            self.assertClose(
+                new_mesh.faces_normals_padded(),
+                new_mesh_naive.faces_normals_padded(),
+            )
+            self.assertClose(
+                new_mesh.faces_areas_packed(),
+                new_mesh_naive.faces_areas_packed(),
+            )
+
+    def test_scale_verts(self):
+        def naive_scale_verts(mesh, scale):
+            if not torch.is_tensor(scale):
+                scale = torch.ones(len(mesh)).mul_(scale)
+            # new Meshes class
+            new_verts_list = [
+                scale[i] * v.clone() for (i, v) in enumerate(mesh.verts_list())
+            ]
+            new_faces_list = [f.clone() for f in mesh.faces_list()]
+            return Meshes(verts=new_verts_list, faces=new_faces_list)
+
+        N = 5
+        for test in ["tensor", "scalar"]:
+            mesh = TestMeshes.init_mesh(N, 10, 100)
+            for force in [0, 1]:
+                if force:
+                    # force mesh to have computed attributes
+                    mesh.verts_packed()
+                    mesh.edges_packed()
+                    mesh.verts_padded()
+                    mesh._compute_face_areas_normals(refresh=True)
+                    mesh._compute_vertex_normals(refresh=True)
+
+                if test == "tensor":
+                    scales = torch.rand(N)
+                elif test == "scalar":
+                    scales = torch.rand(1)[0].item()
+                new_mesh_naive = naive_scale_verts(mesh, scales)
+                new_mesh = mesh.scale_verts(scales)
+                for i in range(N):
+                    if test == "tensor":
+                        self.assertClose(
+                            scales[i] * mesh.verts_list()[i],
+                            new_mesh.verts_list()[i],
+                        )
+                    else:
+                        self.assertClose(
+                            scales * mesh.verts_list()[i],
+                            new_mesh.verts_list()[i],
+                        )
+                    self.assertClose(
+                        new_mesh.verts_list()[i], new_mesh_naive.verts_list()[i]
+                    )
+                    self.assertClose(
+                        mesh.faces_list()[i], new_mesh_naive.faces_list()[i]
+                    )
+                    self.assertClose(
+                        new_mesh.faces_list()[i], new_mesh_naive.faces_list()[i]
+                    )
+                    # check face and vertex normals
+                    self.assertClose(
+                        new_mesh.verts_normals_list()[i],
+                        new_mesh_naive.verts_normals_list()[i],
+                    )
+                    self.assertClose(
+                        new_mesh.faces_normals_list()[i],
+                        new_mesh_naive.faces_normals_list()[i],
+                    )
+
+                # check padded & packed
+                self.assertClose(
+                    new_mesh.faces_padded(), new_mesh_naive.faces_padded()
+                )
+                self.assertClose(
+                    new_mesh.verts_padded(), new_mesh_naive.verts_padded()
+                )
+                self.assertClose(
+                    new_mesh.faces_packed(), new_mesh_naive.faces_packed()
+                )
+                self.assertClose(
+                    new_mesh.verts_packed(), new_mesh_naive.verts_packed()
+                )
+                self.assertClose(
+                    new_mesh.edges_packed(), new_mesh_naive.edges_packed()
+                )
+                self.assertClose(
+                    new_mesh.verts_packed_to_mesh_idx(),
+                    new_mesh_naive.verts_packed_to_mesh_idx(),
+                )
+                self.assertClose(
+                    new_mesh.mesh_to_verts_packed_first_idx(),
+                    new_mesh_naive.mesh_to_verts_packed_first_idx(),
+                )
+                self.assertClose(
+                    new_mesh.num_verts_per_mesh(),
+                    new_mesh_naive.num_verts_per_mesh(),
+                )
+                self.assertClose(
+                    new_mesh.faces_packed_to_mesh_idx(),
+                    new_mesh_naive.faces_packed_to_mesh_idx(),
+                )
+                self.assertClose(
+                    new_mesh.mesh_to_faces_packed_first_idx(),
+                    new_mesh_naive.mesh_to_faces_packed_first_idx(),
+                )
+                self.assertClose(
+                    new_mesh.num_faces_per_mesh(),
+                    new_mesh_naive.num_faces_per_mesh(),
+                )
+                self.assertClose(
+                    new_mesh.edges_packed_to_mesh_idx(),
+                    new_mesh_naive.edges_packed_to_mesh_idx(),
+                )
+                self.assertClose(
+                    new_mesh.verts_padded_to_packed_idx(),
+                    new_mesh_naive.verts_padded_to_packed_idx(),
+                )
+                self.assertTrue(all(new_mesh.valid == new_mesh_naive.valid))
+                self.assertTrue(new_mesh.equisized == new_mesh_naive.equisized)
+
+                # check face areas, normals and vertex normals
+                self.assertClose(
+                    new_mesh.verts_normals_packed(),
+                    new_mesh_naive.verts_normals_packed(),
+                )
+                self.assertClose(
+                    new_mesh.verts_normals_padded(),
+                    new_mesh_naive.verts_normals_padded(),
+                )
+                self.assertClose(
+                    new_mesh.faces_normals_packed(),
+                    new_mesh_naive.faces_normals_packed(),
+                )
+                self.assertClose(
+                    new_mesh.faces_normals_padded(),
+                    new_mesh_naive.faces_normals_padded(),
+                )
+                self.assertClose(
+                    new_mesh.faces_areas_packed(),
+                    new_mesh_naive.faces_areas_packed(),
+                )
+
+    def test_extend_list(self):
+        N = 10
+        mesh = TestMeshes.init_mesh(5, 10, 100)
+        for force in [0, 1]:
+            if force:
+                # force some computes to happen
+                mesh._compute_packed(refresh=True)
+                mesh._compute_padded()
+                mesh._compute_edges_packed()
+                mesh.verts_padded_to_packed_idx()
+            new_mesh = mesh.extend(N)
+            self.assertEqual(len(mesh) * 10, len(new_mesh))
+            for i in range(len(mesh)):
+                for n in range(N):
+                    self.assertClose(
+                        mesh.verts_list()[i], new_mesh.verts_list()[i * N + n]
+                    )
+                    self.assertClose(
+                        mesh.faces_list()[i], new_mesh.faces_list()[i * N + n]
+                    )
+                    self.assertTrue(mesh.valid[i] == new_mesh.valid[i * N + n])
+            self.assertAllSeparate(
+                mesh.verts_list()
+                + new_mesh.verts_list()
+                + mesh.faces_list()
+                + new_mesh.faces_list()
+            )
+            self.assertTrue(new_mesh._verts_packed is None)
+            self.assertTrue(new_mesh._faces_packed is None)
+            self.assertTrue(new_mesh._verts_padded is None)
+            self.assertTrue(new_mesh._faces_padded is None)
+            self.assertTrue(new_mesh._edges_packed is None)
+
+        with self.assertRaises(ValueError):
+            mesh.extend(N=-1)
+
+    def test_to(self):
+        mesh = TestMeshes.init_mesh(5, 10, 100, device=torch.device("cuda:0"))
+        device = torch.device("cuda:1")
+
+        new_mesh = mesh.to(device)
+        self.assertTrue(new_mesh.device == device)
+        self.assertTrue(mesh.device == torch.device("cuda:0"))
+
+    def test_split_mesh(self):
+        mesh = TestMeshes.init_mesh(5, 10, 100)
+        split_sizes = [2, 3]
+        split_meshes = mesh.split(split_sizes)
+        self.assertTrue(len(split_meshes[0]) == 2)
+        self.assertTrue(
+            split_meshes[0].verts_list()
+            == [
+                mesh.get_mesh_verts_faces(0)[0],
+                mesh.get_mesh_verts_faces(1)[0],
+            ]
+        )
+        self.assertTrue(len(split_meshes[1]) == 3)
+        self.assertTrue(
+            split_meshes[1].verts_list()
+            == [
+                mesh.get_mesh_verts_faces(2)[0],
+                mesh.get_mesh_verts_faces(3)[0],
+                mesh.get_mesh_verts_faces(4)[0],
+            ]
+        )
+
+        split_sizes = [2, 0.3]
+        with self.assertRaises(ValueError):
+            mesh.split(split_sizes)
+
+    def test_get_mesh_verts_faces(self):
+        device = torch.device("cuda:0")
+        verts_list = []
+        faces_list = []
+        verts_faces = [(10, 100), (20, 200)]
+        for (V, F) in verts_faces:
+            verts = torch.rand((V, 3), dtype=torch.float32, device=device)
+            faces = torch.randint(
+                V, size=(F, 3), dtype=torch.int64, device=device
+            )
+            verts_list.append(verts)
+            faces_list.append(faces)
+
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+
+        for i, (V, F) in enumerate(verts_faces):
+            verts, faces = mesh.get_mesh_verts_faces(i)
+            self.assertTrue(len(verts) == V)
+            self.assertClose(verts, verts_list[i])
+            self.assertTrue(len(faces) == F)
+            self.assertClose(faces, faces_list[i])
+
+        with self.assertRaises(ValueError):
+            mesh.get_mesh_verts_faces(5)
+        with self.assertRaises(ValueError):
+            mesh.get_mesh_verts_faces(0.2)
+
+    def test_get_bounding_boxes(self):
+        device = torch.device("cuda:0")
+        verts_list = []
+        faces_list = []
+        for (V, F) in [(10, 100)]:
+            verts = torch.rand((V, 3), dtype=torch.float32, device=device)
+            faces = torch.randint(
+                V, size=(F, 3), dtype=torch.int64, device=device
+            )
+            verts_list.append(verts)
+            faces_list.append(faces)
+
+        mins = torch.min(verts, dim=0)[0]
+        maxs = torch.max(verts, dim=0)[0]
+        bboxes_gt = torch.stack([mins, maxs], dim=1).unsqueeze(0)
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+        bboxes = mesh.get_bounding_boxes()
+        self.assertClose(bboxes_gt, bboxes)
+
+    def test_padded_to_packed_idx(self):
+        device = torch.device("cuda:0")
+        verts_list = []
+        faces_list = []
+        verts_faces = [(10, 100), (20, 200), (30, 300)]
+        for (V, F) in verts_faces:
+            verts = torch.rand((V, 3), dtype=torch.float32, device=device)
+            faces = torch.randint(
+                V, size=(F, 3), dtype=torch.int64, device=device
+            )
+            verts_list.append(verts)
+            faces_list.append(faces)
+
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+        verts_padded_to_packed_idx = mesh.verts_padded_to_packed_idx()
+        verts_packed = mesh.verts_packed()
+        verts_padded = mesh.verts_padded()
+        verts_padded_flat = verts_padded.view(-1, 3)
+
+        self.assertClose(
+            verts_padded_flat[verts_padded_to_packed_idx], verts_packed
+        )
+
+        idx = verts_padded_to_packed_idx.view(-1, 1).expand(-1, 3)
+        self.assertClose(verts_padded_flat.gather(0, idx), verts_packed)
+
+    def test_getitem(self):
+        device = torch.device("cuda:0")
+        verts_list = []
+        faces_list = []
+        verts_faces = [(10, 100), (20, 200), (30, 300)]
+        for (V, F) in verts_faces:
+            verts = torch.rand((V, 3), dtype=torch.float32, device=device)
+            faces = torch.randint(
+                V, size=(F, 3), dtype=torch.int64, device=device
+            )
+            verts_list.append(verts)
+            faces_list.append(faces)
+
+        mesh = Meshes(verts=verts_list, faces=faces_list)
+
+        def check_equal(selected, indices):
+            for selectedIdx, index in enumerate(indices):
+                self.assertClose(
+                    selected.verts_list()[selectedIdx], mesh.verts_list()[index]
+                )
+                self.assertClose(
+                    selected.faces_list()[selectedIdx], mesh.faces_list()[index]
+                )
+
+        # int index
+        index = 1
+        mesh_selected = mesh[index]
+        self.assertTrue(len(mesh_selected) == 1)
+        check_equal(mesh_selected, [index])
+
+        # list index
+        index = [1, 2]
+        mesh_selected = mesh[index]
+        self.assertTrue(len(mesh_selected) == len(index))
+        check_equal(mesh_selected, index)
+
+        # slice index
+        index = slice(0, 2, 1)
+        mesh_selected = mesh[index]
+        check_equal(mesh_selected, [0, 1])
+
+        # bool tensor
+        index = torch.tensor([1, 0, 1], dtype=torch.bool, device=device)
+        mesh_selected = mesh[index]
+        self.assertTrue(len(mesh_selected) == index.sum())
+        check_equal(mesh_selected, [0, 2])
+
+        # int tensor
+        index = torch.tensor([1, 2], dtype=torch.int64, device=device)
+        mesh_selected = mesh[index]
+        self.assertTrue(len(mesh_selected) == index.numel())
+        check_equal(mesh_selected, index.tolist())
+
+        # invalid index
+        index = torch.tensor([1, 0, 1], dtype=torch.float32, device=device)
+        with self.assertRaises(IndexError):
+            mesh_selected = mesh[index]
+        index = 1.2
+        with self.assertRaises(IndexError):
+            mesh_selected = mesh[index]
+
+    def test_compute_faces_areas(self):
+        verts = torch.tensor(
+            [
+                [0.0, 0.0, 0.0],
+                [0.5, 0.0, 0.0],
+                [0.5, 0.5, 0.0],
+                [0.5, 0.0, 0.0],
+                [0.25, 0.8, 0.0],
+            ],
+            dtype=torch.float32,
+        )
+        faces = torch.tensor([[0, 1, 2], [0, 3, 4]], dtype=torch.int64)
+        mesh = Meshes(verts=[verts], faces=[faces])
+
+        face_areas = mesh.faces_areas_packed()
+        expected_areas = torch.tensor([0.125, 0.2])
+        self.assertTrue(torch.allclose(face_areas, expected_areas))
+
+    def test_compute_normals(self):
+
+        # Simple case with one mesh where normals point in either +/- ijk
+        verts = torch.tensor(
+            [
+                [0.1, 0.3, 0.0],
+                [0.5, 0.2, 0.0],
+                [0.6, 0.8, 0.0],
+                [0.0, 0.3, 0.2],
+                [0.0, 0.2, 0.5],
+                [0.0, 0.8, 0.7],
+                [0.5, 0.0, 0.2],
+                [0.6, 0.0, 0.5],
+                [0.8, 0.0, 0.7],
+                [0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0],
+            ],
+            dtype=torch.float32,
+        )
+        faces = torch.tensor(
+            [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]], dtype=torch.int64
+        )
+        mesh = Meshes(verts=[verts], faces=[faces])
+
+        verts_normals_expected = torch.tensor(
+            [
+                [0.0, 0.0, 1.0],
+                [0.0, 0.0, 1.0],
+                [0.0, 0.0, 1.0],
+                [-1.0, 0.0, 0.0],
+                [-1.0, 0.0, 0.0],
+                [-1.0, 0.0, 0.0],
+                [0.0, 1.0, 0.0],
+                [0.0, 1.0, 0.0],
+                [0.0, 1.0, 0.0],
+                [0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0],
+            ]
+        )
+        faces_normals_expected = verts_normals_expected[[0, 3, 6, 9], :]
+
+        self.assertTrue(
+            torch.allclose(mesh.verts_normals_list()[0], verts_normals_expected)
+        )
+        self.assertTrue(
+            torch.allclose(mesh.faces_normals_list()[0], faces_normals_expected)
+        )
+        self.assertTrue(
+            torch.allclose(mesh.verts_normals_packed(), verts_normals_expected)
+        )
+        self.assertTrue(
+            torch.allclose(mesh.faces_normals_packed(), faces_normals_expected)
+        )
+
+        # Multiple meshes in the batch with equal sized meshes
+        meshes_extended = mesh.extend(3)
+        for m in meshes_extended.verts_normals_list():
+            self.assertTrue(torch.allclose(m, verts_normals_expected))
+        for f in meshes_extended.faces_normals_list():
+            self.assertTrue(torch.allclose(f, faces_normals_expected))
+
+        # Multiple meshes in the batch with different sized meshes
+        # Check padded and packed normals are the correct sizes.
+        verts2 = torch.tensor(
+            [
+                [0.1, 0.3, 0.0],
+                [0.5, 0.2, 0.0],
+                [0.6, 0.8, 0.0],
+                [0.0, 0.3, 0.2],
+                [0.0, 0.2, 0.5],
+                [0.0, 0.8, 0.7],
+            ],
+            dtype=torch.float32,
+        )
+        faces2 = torch.tensor([[0, 1, 2], [3, 4, 5]], dtype=torch.int64)
+        verts_list = [verts, verts2]
+        faces_list = [faces, faces2]
+        meshes = Meshes(verts=verts_list, faces=faces_list)
+        verts_normals_padded = meshes.verts_normals_padded()
+        faces_normals_padded = meshes.faces_normals_padded()
+
+        for n in range(len(meshes)):
+            v = verts_list[n].shape[0]
+            f = faces_list[n].shape[0]
+            if verts_normals_padded.shape[1] > v:
+                self.assertTrue(verts_normals_padded[n, v:, :].eq(0).all())
+                self.assertTrue(
+                    torch.allclose(
+                        verts_normals_padded[n, :v, :].view(-1, 3),
+                        verts_normals_expected[:v, :],
+                    )
+                )
+            if faces_normals_padded.shape[1] > f:
+                self.assertTrue(faces_normals_padded[n, f:, :].eq(0).all())
+                self.assertTrue(
+                    torch.allclose(
+                        faces_normals_padded[n, :f, :].view(-1, 3),
+                        faces_normals_expected[:f, :],
+                    )
+                )
+
+        verts_normals_packed = meshes.verts_normals_packed()
+        faces_normals_packed = meshes.faces_normals_packed()
+        self.assertTrue(
+            list(verts_normals_packed.shape)
+            == [verts.shape[0] + verts2.shape[0], 3]
+        )
+        self.assertTrue(
+            list(faces_normals_packed.shape)
+            == [faces.shape[0] + faces2.shape[0], 3]
+        )
+
+        # Single mesh where two faces share one vertex so the normal is
+        # the weighted sum of the two face normals.
+        verts = torch.tensor(
+            [
+                [0.1, 0.3, 0.0],
+                [0.5, 0.2, 0.0],
+                [0.0, 0.3, 0.2],  # vertex is shared between two faces
+                [0.0, 0.2, 0.5],
+                [0.0, 0.8, 0.7],
+            ],
+            dtype=torch.float32,
+        )
+        faces = torch.tensor([[0, 1, 2], [2, 3, 4]], dtype=torch.int64)
+        mesh = Meshes(verts=[verts], faces=[faces])
+
+        verts_normals_expected = torch.tensor(
+            [
+                [-0.2408, -0.9631, -0.1204],
+                [-0.2408, -0.9631, -0.1204],
+                [-0.9389, -0.3414, -0.0427],
+                [-1.0000, 0.0000, 0.0000],
+                [-1.0000, 0.0000, 0.0000],
+            ]
+        )
+        faces_normals_expected = torch.tensor(
+            [[-0.2408, -0.9631, -0.1204], [-1.0000, 0.0000, 0.0000]]
+        )
+        self.assertTrue(
+            torch.allclose(
+                mesh.verts_normals_list()[0], verts_normals_expected, atol=4e-5
+            )
+        )
+        self.assertTrue(
+            torch.allclose(
+                mesh.faces_normals_list()[0], faces_normals_expected, atol=4e-5
+            )
+        )
+
+        # Check empty mesh has empty normals
+        meshes = Meshes(verts=[], faces=[])
+        self.assertEqual(meshes.verts_normals_packed().shape[0], 0)
+        self.assertEqual(meshes.verts_normals_padded().shape[0], 0)
+        self.assertEqual(meshes.verts_normals_list(), [])
+        self.assertEqual(meshes.faces_normals_packed().shape[0], 0)
+        self.assertEqual(meshes.faces_normals_padded().shape[0], 0)
+        self.assertEqual(meshes.faces_normals_list(), [])
+
+    def test_compute_faces_areas_cpu_cuda(self):
+        num_meshes = 10
+        max_v = 100
+        max_f = 300
+        mesh_cpu = TestMeshes.init_mesh(num_meshes, max_v, max_f, device="cpu")
+        device = torch.device("cuda:0")
+        mesh_cuda = mesh_cpu.to(device)
+
+        face_areas_cpu = mesh_cpu.faces_areas_packed()
+        face_normals_cpu = mesh_cpu.faces_normals_packed()
+        face_areas_cuda = mesh_cuda.faces_areas_packed()
+        face_normals_cuda = mesh_cuda.faces_normals_packed()
+        self.assertClose(face_areas_cpu, face_areas_cuda.cpu(), atol=1e-6)
+        # because of the normalization of the normals with arbitrarily small values,
+        # normals can become unstable. Thus only compare normals, for faces
+        # with areas > eps=1e-6
+        nonzero = face_areas_cpu > 1e-6
+        self.assertClose(
+            face_normals_cpu[nonzero],
+            face_normals_cuda.cpu()[nonzero],
+            atol=1e-6,
+        )
+
+    @staticmethod
+    def compute_packed_with_init(
+        num_meshes: int = 10,
+        max_v: int = 100,
+        max_f: int = 300,
+        device: str = "cpu",
+    ):
+        mesh = TestMeshes.init_mesh(num_meshes, max_v, max_f, device=device)
+        torch.cuda.synchronize()
+
+        def compute_packed():
+            mesh._compute_packed(refresh=True)
+            torch.cuda.synchronize()
+
+        return compute_packed
+
+    @staticmethod
+    def compute_padded_with_init(
+        num_meshes: int = 10,
+        max_v: int = 100,
+        max_f: int = 300,
+        device: str = "cpu",
+    ):
+        mesh = TestMeshes.init_mesh(num_meshes, max_v, max_f, device=device)
+        torch.cuda.synchronize()
+
+        def compute_padded():
+            mesh._compute_padded(refresh=True)
+            torch.cuda.synchronize()
+
+        return compute_padded

tests/test_nearest_neighbor_points.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import unittest
+import torch
+
+from pytorch3d import _C
+
+
+class TestNearestNeighborPoints(unittest.TestCase):
+    @staticmethod
+    def nn_points_idx_naive(x, y):
+        """
+        PyTorch implementation of nn_points_idx function.
+        """
+        N, P1, D = x.shape
+        _N, P2, _D = y.shape
+        assert N == _N and D == _D
+        diffs = x.view(N, P1, 1, D) - y.view(N, 1, P2, D)
+        dists2 = (diffs * diffs).sum(3)
+        idx = dists2.argmin(2)
+        return idx
+
+    def test_nn_cuda(self):
+        """
+        Test cuda output vs naive python implementation.
+        """
+        device = torch.device("cuda:0")
+        for D in [3, 4]:
+            for N in [1, 4]:
+                for P1 in [1, 8, 64, 128]:
+                    for P2 in [32, 128]:
+                        x = torch.randn(N, P1, D, device=device)
+                        y = torch.randn(N, P2, D, device=device)
+
+                        # _C.nn_points_idx should dispatch
+                        # to the cpp or cuda versions of the function
+                        # depending on the input type.
+                        idx1 = _C.nn_points_idx(x, y)
+                        idx2 = TestNearestNeighborPoints.nn_points_idx_naive(
+                            x, y
+                        )
+                        self.assertTrue(idx1.size(1) == P1)
+                        self.assertTrue(torch.all(idx1 == idx2))
+
+    def test_nn_cuda_error(self):
+        """
+        Check that nn_points_idx throws an error if cpu tensors
+        are given as input.
+        """
+        x = torch.randn(1, 1, 3)
+        y = torch.randn(1, 1, 3)
+        with self.assertRaises(Exception) as err:
+            _C.nn_points_idx(x, y)
+        self.assertTrue("Not implemented on the CPU" in str(err.exception))
+
+    @staticmethod
+    def bm_nn_points_cuda_with_init(
+        N: int = 4, D: int = 4, P1: int = 128, P2: int = 128
+    ):
+        device = torch.device("cuda:0")
+        x = torch.randn(N, P1, D, device=device)
+        y = torch.randn(N, P2, D, device=device)
+        torch.cuda.synchronize()
+
+        def nn_cpp():
+            _C.nn_points_idx(x.contiguous(), y.contiguous())
+            torch.cuda.synchronize()
+
+        return nn_cpp
+
+    @staticmethod
+    def bm_nn_points_python_with_init(
+        N: int = 4, D: int = 4, P1: int = 128, P2: int = 128
+    ):
+        x = torch.randn(N, P1, D)
+        y = torch.randn(N, P2, D)
+
+        def nn_python():
+            TestNearestNeighborPoints.nn_points_idx_naive(x, y)
+
+        return nn_python

tests/test_obj_io.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import os
+import unittest
+from io import StringIO
+from pathlib import Path
+import torch
+
+from pytorch3d.io import load_obj, save_obj
+
+
+class TestMeshObjIO(unittest.TestCase):
+    def test_load_obj_simple(self):
+        obj_file = "\n".join(
+            [
+                "# this is a comment",  # Comments should be ignored.
+                "v 0.1 0.2 0.3",
+                "v 0.2 0.3 0.4",
+                "v 0.3 0.4 0.5",
+                "v  0.4 0.5 0.6",  # some obj files have multiple spaces after v
+                "f 1 2 3",
+                "f 1 2 4 3 1",  # Polygons should be split into triangles
+            ]
+        )
+        obj_file = StringIO(obj_file)
+        verts, faces, aux = load_obj(obj_file)
+        normals = aux.normals
+        textures = aux.verts_uvs
+        materials = aux.material_colors
+        tex_maps = aux.texture_images
+
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+            ],
+            dtype=torch.float32,
+        )
+        expected_faces = torch.tensor(
+            [
+                [0, 1, 2],  # First face
+                [0, 1, 3],  # Second face (polygon)
+                [0, 3, 2],  # Second face (polygon)
+                [0, 2, 0],  # Second face (polygon)
+            ],
+            dtype=torch.int64,
+        )
+        self.assertTrue(torch.all(verts == expected_verts))
+        self.assertTrue(torch.all(faces.verts_idx == expected_faces))
+        self.assertTrue(faces.normals_idx == [])
+        self.assertTrue(faces.textures_idx == [])
+        self.assertTrue(
+            torch.all(faces.materials_idx == -torch.ones(len(expected_faces)))
+        )
+        self.assertTrue(normals is None)
+        self.assertTrue(textures is None)
+        self.assertTrue(materials is None)
+        self.assertTrue(tex_maps is None)
+
+    def test_load_obj_complex(self):
+        obj_file = "\n".join(
+            [
+                "# this is a comment",  # Comments should be ignored.
+                "v 0.1 0.2 0.3",
+                "v 0.2 0.3 0.4",
+                "v 0.3 0.4 0.5",
+                "v 0.4 0.5 0.6",
+                "vn 0.000000 0.000000 -1.000000",
+                "vn -1.000000 -0.000000 -0.000000",
+                "vn -0.000000 -0.000000 1.000000",  # Normals should not be ignored.
+                "v 0.5 0.6 0.7",
+                "vt 0.749279 0.501284 0.0",  # Some files add 0.0 - ignore this.
+                "vt 0.999110 0.501077",
+                "vt 0.999455 0.750380",
+                "f 1 2 3",
+                "f 1 2 4 3 5",  # Polygons should be split into triangles
+                "f 2/1/2 3/1/2 4/2/2",  # Texture/normals are loaded correctly.
+                "f -1 -2 1",  # Negative indexing counts from the end.
+            ]
+        )
+        obj_file = StringIO(obj_file)
+        verts, faces, aux = load_obj(obj_file)
+        normals = aux.normals
+        textures = aux.verts_uvs
+        materials = aux.material_colors
+        tex_maps = aux.texture_images
+
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+                [0.5, 0.6, 0.7],
+            ],
+            dtype=torch.float32,
+        )
+        expected_faces = torch.tensor(
+            [
+                [0, 1, 2],  # First face
+                [0, 1, 3],  # Second face (polygon)
+                [0, 3, 2],  # Second face (polygon)
+                [0, 2, 4],  # Second face (polygon)
+                [1, 2, 3],  # Third face (normals / texture)
+                [4, 3, 0],  # Fourth face (negative indices)
+            ],
+            dtype=torch.int64,
+        )
+        expected_normals = torch.tensor(
+            [
+                [0.000000, 0.000000, -1.000000],
+                [-1.000000, -0.000000, -0.000000],
+                [-0.000000, -0.000000, 1.000000],
+            ],
+            dtype=torch.float32,
+        )
+        expected_textures = torch.tensor(
+            [[0.749279, 0.501284], [0.999110, 0.501077], [0.999455, 0.750380]],
+            dtype=torch.float32,
+        )
+        expected_faces_normals_idx = torch.tensor(
+            [[1, 1, 1]], dtype=torch.int64
+        )
+        expected_faces_textures_idx = torch.tensor(
+            [[0, 0, 1]], dtype=torch.int64
+        )
+
+        self.assertTrue(torch.all(verts == expected_verts))
+        self.assertTrue(torch.all(faces.verts_idx == expected_faces))
+        self.assertTrue(torch.allclose(normals, expected_normals))
+        self.assertTrue(torch.allclose(textures, expected_textures))
+        self.assertTrue(
+            torch.allclose(faces.normals_idx, expected_faces_normals_idx)
+        )
+        self.assertTrue(
+            torch.allclose(faces.textures_idx, expected_faces_textures_idx)
+        )
+        self.assertTrue(materials is None)
+        self.assertTrue(tex_maps is None)
+
+    def test_load_obj_normals_only(self):
+        obj_file = "\n".join(
+            [
+                "v 0.1 0.2 0.3",
+                "v 0.2 0.3 0.4",
+                "v 0.3 0.4 0.5",
+                "v 0.4 0.5 0.6",
+                "vn 0.000000 0.000000 -1.000000",
+                "vn -1.000000 -0.000000 -0.000000",
+                "f 2//1 3//1 4//2",
+            ]
+        )
+        obj_file = StringIO(obj_file)
+        expected_faces_normals_idx = torch.tensor(
+            [[0, 0, 1]], dtype=torch.int64
+        )
+        expected_normals = torch.tensor(
+            [
+                [0.000000, 0.000000, -1.000000],
+                [-1.000000, -0.000000, -0.000000],
+            ],
+            dtype=torch.float32,
+        )
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+            ],
+            dtype=torch.float32,
+        )
+        verts, faces, aux = load_obj(obj_file)
+        normals = aux.normals
+        textures = aux.verts_uvs
+        materials = aux.material_colors
+        tex_maps = aux.texture_images
+        self.assertTrue(
+            torch.allclose(faces.normals_idx, expected_faces_normals_idx)
+        )
+        self.assertTrue(torch.allclose(normals, expected_normals))
+        self.assertTrue(torch.allclose(verts, expected_verts))
+        self.assertTrue(faces.textures_idx == [])
+        self.assertTrue(textures is None)
+        self.assertTrue(materials is None)
+        self.assertTrue(tex_maps is None)
+
+    def test_load_obj_textures_only(self):
+        obj_file = "\n".join(
+            [
+                "v 0.1 0.2 0.3",
+                "v 0.2 0.3 0.4",
+                "v 0.3 0.4 0.5",
+                "v 0.4 0.5 0.6",
+                "vt 0.999110 0.501077",
+                "vt 0.999455 0.750380",
+                "f 2/1 3/1 4/2",
+            ]
+        )
+        obj_file = StringIO(obj_file)
+        expected_faces_textures_idx = torch.tensor(
+            [[0, 0, 1]], dtype=torch.int64
+        )
+        expected_textures = torch.tensor(
+            [[0.999110, 0.501077], [0.999455, 0.750380]], dtype=torch.float32
+        )
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+            ],
+            dtype=torch.float32,
+        )
+        verts, faces, aux = load_obj(obj_file)
+        normals = aux.normals
+        textures = aux.verts_uvs
+        materials = aux.material_colors
+        tex_maps = aux.texture_images
+
+        self.assertTrue(
+            torch.allclose(faces.textures_idx, expected_faces_textures_idx)
+        )
+        self.assertTrue(torch.allclose(expected_textures, textures))
+        self.assertTrue(torch.allclose(expected_verts, verts))
+        self.assertTrue(faces.normals_idx == [])
+        self.assertTrue(normals is None)
+        self.assertTrue(materials is None)
+        self.assertTrue(tex_maps is None)
+
+    def test_load_obj_error_textures(self):
+        obj_file = "\n".join(["vt 0.1"])
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue("does not have 2 values" in str(err.exception))
+
+    def test_load_obj_error_normals(self):
+        obj_file = "\n".join(["vn 0.1"])
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue("does not have 3 values" in str(err.exception))
+
+    def test_load_obj_error_vertices(self):
+        obj_file = "\n".join(["v 1"])
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue("does not have 3 values" in str(err.exception))
+
+    def test_load_obj_error_inconsistent_triplets(self):
+        obj_file = "\n".join(["f 2//1 3/1 4/1/2"])
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue(
+            "Vertex properties are inconsistent" in str(err.exception)
+        )
+
+    def test_load_obj_error_too_many_vertex_properties(self):
+        obj_file = "\n".join(["f 2/1/1/3"])
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue(
+            "Face vertices can ony have 3 properties" in str(err.exception)
+        )
+
+    def test_load_obj_error_invalid_vertex_indices(self):
+        obj_file = "\n".join(
+            ["v 0.1 0.2 0.3", "v 0.1 0.2 0.3", "v 0.1 0.2 0.3", "f -2 5 1"]
+        )
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue("Faces have invalid indices." in str(err.exception))
+
+    def test_load_obj_error_invalid_normal_indices(self):
+        obj_file = "\n".join(
+            [
+                "v 0.1 0.2 0.3",
+                "v 0.1 0.2 0.3",
+                "v 0.1 0.2 0.3",
+                "vn 0.1 0.2 0.3",
+                "vn 0.1 0.2 0.3",
+                "vn 0.1 0.2 0.3",
+                "f -2/2 2/4 1/1",
+            ]
+        )
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue("Faces have invalid indices." in str(err.exception))
+
+    def test_load_obj_error_invalid_texture_indices(self):
+        obj_file = "\n".join(
+            [
+                "v 0.1 0.2 0.3",
+                "v 0.1 0.2 0.3",
+                "v 0.1 0.2 0.3",
+                "vt 0.1 0.2",
+                "vt 0.1 0.2",
+                "vt 0.1 0.2",
+                "f -2//2 2//6 1//1",
+            ]
+        )
+        obj_file = StringIO(obj_file)
+
+        with self.assertRaises(ValueError) as err:
+            load_obj(obj_file)
+        self.assertTrue("Faces have invalid indices." in str(err.exception))
+
+    def test_save_obj(self):
+        verts = torch.tensor(
+            [
+                [0.01, 0.2, 0.301],
+                [0.2, 0.03, 0.408],
+                [0.3, 0.4, 0.05],
+                [0.6, 0.7, 0.8],
+            ],
+            dtype=torch.float32,
+        )
+        faces = torch.tensor(
+            [[0, 2, 1], [0, 1, 2], [3, 2, 1], [3, 1, 0]], dtype=torch.int64
+        )
+        obj_file = StringIO()
+        save_obj(obj_file, verts, faces, decimal_places=2)
+        expected_file = "\n".join(
+            [
+                "v 0.01 0.20 0.30",
+                "v 0.20 0.03 0.41",
+                "v 0.30 0.40 0.05",
+                "v 0.60 0.70 0.80",
+                "f 1 3 2",
+                "f 1 2 3",
+                "f 4 3 2",
+                "f 4 2 1",
+            ]
+        )
+        actual_file = obj_file.getvalue()
+        self.assertEqual(actual_file, expected_file)
+
+    def test_load_mtl(self):
+        DATA_DIR = (
+            Path(__file__).resolve().parent.parent / "docs/tutorials/data"
+        )
+        obj_filename = "cow_mesh/cow.obj"
+        filename = os.path.join(DATA_DIR, obj_filename)
+        verts, faces, aux = load_obj(filename)
+        materials = aux.material_colors
+        tex_maps = aux.texture_images
+
+        dtype = torch.float32
+        expected_materials = {
+            "material_1": {
+                "ambient_color": torch.tensor([1.0, 1.0, 1.0], dtype=dtype),
+                "diffuse_color": torch.tensor([1.0, 1.0, 1.0], dtype=dtype),
+                "specular_color": torch.tensor([0.0, 0.0, 0.0], dtype=dtype),
+                "shininess": torch.tensor([10.0], dtype=dtype),
+            }
+        }
+        # Check that there is an image with material name material_1.
+        self.assertTrue(tuple(tex_maps.keys()) == ("material_1",))
+        self.assertTrue(torch.is_tensor(tuple(tex_maps.values())[0]))
+        self.assertTrue(
+            torch.all(faces.materials_idx == torch.zeros(len(faces.verts_idx)))
+        )
+
+        # Check all keys and values in dictionary are the same.
+        for n1, n2 in zip(materials.keys(), expected_materials.keys()):
+            self.assertTrue(n1 == n2)
+            for k1, k2 in zip(
+                materials[n1].keys(), expected_materials[n2].keys()
+            ):
+                self.assertTrue(
+                    torch.allclose(
+                        materials[n1][k1], expected_materials[n2][k2]
+                    )
+                )
+
+    def test_load_mtl_fail(self):
+        # Faces have a material
+        obj_file = "\n".join(
+            [
+                "v 0.1 0.2 0.3",
+                "v 0.2 0.3 0.4",
+                "v 0.3 0.4 0.5",
+                "v 0.4 0.5 0.6",
+                "usemtl material_1",
+                "f 1 2 3",
+                "f 1 2 4",
+            ]
+        )
+        obj_file = StringIO(obj_file)
+        with self.assertWarnsRegex(Warning, "No mtl file provided"):
+            verts, faces, aux = load_obj(obj_file)
+
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+            ],
+            dtype=torch.float32,
+        )
+        expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
+        self.assertTrue(torch.allclose(verts, expected_verts))
+        self.assertTrue(torch.allclose(faces.verts_idx, expected_faces))
+        self.assertTrue(aux.material_colors is None)
+        self.assertTrue(aux.texture_images is None)
+        self.assertTrue(aux.normals is None)
+        self.assertTrue(aux.verts_uvs is None)
+
+    def test_load_obj_missing_texture(self):
+        DATA_DIR = Path(__file__).resolve().parent / "data"
+        obj_filename = "missing_files_obj/model.obj"
+        filename = os.path.join(DATA_DIR, obj_filename)
+        with self.assertWarnsRegex(Warning, "Texture file does not exist"):
+            verts, faces, aux = load_obj(filename)
+
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+            ],
+            dtype=torch.float32,
+        )
+        expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
+        self.assertTrue(torch.allclose(verts, expected_verts))
+        self.assertTrue(torch.allclose(faces.verts_idx, expected_faces))
+
+    def test_load_obj_missing_mtl(self):
+        DATA_DIR = Path(__file__).resolve().parent / "data"
+        obj_filename = "missing_files_obj/model2.obj"
+        filename = os.path.join(DATA_DIR, obj_filename)
+        with self.assertWarnsRegex(Warning, "Mtl file does not exist"):
+            verts, faces, aux = load_obj(filename)
+
+        expected_verts = torch.tensor(
+            [
+                [0.1, 0.2, 0.3],
+                [0.2, 0.3, 0.4],
+                [0.3, 0.4, 0.5],
+                [0.4, 0.5, 0.6],
+            ],
+            dtype=torch.float32,
+        )
+        expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
+        self.assertTrue(torch.allclose(verts, expected_verts))
+        self.assertTrue(torch.allclose(faces.verts_idx, expected_faces))
+
+    @staticmethod
+    def save_obj_with_init(V: int, F: int):
+        verts_list = torch.tensor(V * [[0.11, 0.22, 0.33]]).view(-1, 3)
+        faces_list = torch.tensor(F * [[1, 2, 3]]).view(-1, 3)
+        obj_file = StringIO()
+
+        def save_mesh():
+            save_obj(obj_file, verts_list, faces_list, decimal_places=2)
+
+        return save_mesh
+
+    @staticmethod
+    def load_obj_with_init(V: int, F: int):
+        obj = "\n".join(["v 0.1 0.2 0.3"] * V + ["f 1 2 3"] * F)
+
+        def load_mesh():
+            obj_file = StringIO(obj)
+            verts, faces, aux = load_obj(obj_file)
+
+        return load_mesh

tests/test_ply_io.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import struct
+import unittest
+from io import BytesIO, StringIO
+import torch
+
+from pytorch3d.io.ply_io import _load_ply_raw, load_ply, save_ply
+
+from common_testing import TestCaseMixin
+
+
+class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
+    def test_raw_load_simple_ascii(self):
+        ply_file = "\n".join(
+            [
+                "ply",
+                "format ascii 1.0",
+                "comment made by Greg Turk",
+                "comment this file is a cube",
+                "element vertex 8",
+                "property float x",
+                "property float y",
+                "property float z",
+                "element face 6",
+                "property list uchar int vertex_index",
+                "element irregular_list 3",
+                "property list uchar int vertex_index",
+                "end_header",
+                "0 0 0",
+                "0 0 1",
+                "0 1 1",
+                "0 1 0",
+                "1 0 0",
+                "1 0 1",
+                "1 1 1",
+                "1 1 0",
+                "4 0 1 2 3",
+                "4 7 6 5 4",
+                "4 0 4 5 1",
+                "4 1 5 6 2",
+                "4 2 6 7 3",
+                "4 3 7 4 0",  # end of faces
+                "4 0 1 2 3",
+                "4 7 6 5 4",
+                "3 4 5 1",
+            ]
+        )
+        for line_ending in [None, "\n", "\r\n"]:
+            if line_ending is None:
+                stream = StringIO(ply_file)
+            else:
+                byte_file = ply_file.encode("ascii")
+                if line_ending == "\r\n":
+                    byte_file = byte_file.replace(b"\n", b"\r\n")
+                stream = BytesIO(byte_file)
+            header, data = _load_ply_raw(stream)
+            self.assertTrue(header.ascii)
+            self.assertEqual(len(data), 3)
+            self.assertTupleEqual(data["face"].shape, (6, 4))
+            self.assertClose([0, 1, 2, 3], data["face"][0])
+            self.assertClose([3, 7, 4, 0], data["face"][5])
+            self.assertTupleEqual(data["vertex"].shape, (8, 3))
+            irregular = data["irregular_list"]
+            self.assertEqual(len(irregular), 3)
+            self.assertEqual(type(irregular), list)
+            [x] = irregular[0]
+            self.assertClose(x, [0, 1, 2, 3])
+            [x] = irregular[1]
+            self.assertClose(x, [7, 6, 5, 4])
+            [x] = irregular[2]
+            self.assertClose(x, [4, 5, 1])
+
+    def test_load_simple_ascii(self):
+        ply_file = "\n".join(
+            [
+                "ply",
+                "format ascii 1.0",
+                "comment made by Greg Turk",
+                "comment this file is a cube",
+                "element vertex 8",
+                "property float x",
+                "property float y",
+                "property float z",
+                "element face 6",
+                "property list uchar int vertex_index",
+                "end_header",
+                "0 0 0",
+                "0 0 1",
+                "0 1 1",
+                "0 1 0",
+                "1 0 0",
+                "1 0 1",
+                "1 1 1",
+                "1 1 0",
+                "4 0 1 2 3",
+                "4 7 6 5 4",
+                "4 0 4 5 1",
+                "4 1 5 6 2",
+                "4 2 6 7 3",
+                "4 3 7 4 0",
+            ]
+        )
+        for line_ending in [None, "\n", "\r\n"]:
+            if line_ending is None:
+                stream = StringIO(ply_file)
+            else:
+                byte_file = ply_file.encode("ascii")
+                if line_ending == "\r\n":
+                    byte_file = byte_file.replace(b"\n", b"\r\n")
+                stream = BytesIO(byte_file)
+            verts, faces = load_ply(stream)
+            self.assertEqual(verts.shape, (8, 3))
+            self.assertEqual(faces.shape, (12, 3))
+            verts_expected = [
+                [0, 0, 0],
+                [0, 0, 1],
+                [0, 1, 1],
+                [0, 1, 0],
+                [1, 0, 0],
+                [1, 0, 1],
+                [1, 1, 1],
+                [1, 1, 0],
+            ]
+            self.assertClose(verts, torch.FloatTensor(verts_expected))
+            faces_expected = [
+                [0, 1, 2],
+                [7, 6, 5],
+                [0, 4, 5],
+                [1, 5, 6],
+                [2, 6, 7],
+                [3, 7, 4],
+                [0, 2, 3],
+                [7, 5, 4],
+                [0, 5, 1],
+                [1, 6, 2],
+                [2, 7, 3],
+                [3, 4, 0],
+            ]
+            self.assertClose(faces, torch.LongTensor(faces_expected))
+
+    def test_simple_save(self):
+        verts = torch.tensor(
+            [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=torch.float32
+        )
+        faces = torch.tensor([[0, 1, 2], [0, 3, 4]])
+        file = StringIO()
+        save_ply(file, verts=verts, faces=faces)
+        file.seek(0)
+        verts2, faces2 = load_ply(file)
+        self.assertClose(verts, verts2)
+        self.assertClose(faces, faces2)
+
+    def test_load_simple_binary(self):
+        for big_endian in [True, False]:
+            verts = (
+                "0 0 0 "
+                "0 0 1 "
+                "0 1 1 "
+                "0 1 0 "
+                "1 0 0 "
+                "1 0 1 "
+                "1 1 1 "
+                "1 1 0"
+            ).split()
+            faces = (
+                "4 0 1 2 3 "
+                "4 7 6 5 4 "
+                "4 0 4 5 1 "
+                "4 1 5 6 2 "
+                "4 2 6 7 3 "
+                "4 3 7 4 0 "  # end of first 6
+                "4 0 1 2 3 "
+                "4 7 6 5 4 "
+                "3 4 5 1"
+            ).split()
+            short_one = b"\00\01" if big_endian else b"\01\00"
+            mixed_data = b"\00\00" b"\03\03" + (
+                short_one + b"\00\01\01\01" b"\00\02"
+            )
+            minus_one_data = b"\xff" * 14
+            endian_char = ">" if big_endian else "<"
+            format = (
+                "format binary_big_endian 1.0"
+                if big_endian
+                else "format binary_little_endian 1.0"
+            )
+            vertex_pattern = endian_char + "24f"
+            vertex_data = struct.pack(vertex_pattern, *map(float, verts))
+            vertex1_pattern = endian_char + "fdffdffdffdffdffdffdffdf"
+            vertex1_data = struct.pack(vertex1_pattern, *map(float, verts))
+            face_char_pattern = endian_char + "44b"
+            face_char_data = struct.pack(face_char_pattern, *map(int, faces))
+            header = "\n".join(
+                [
+                    "ply",
+                    format,
+                    "element vertex 8",
+                    "property float x",
+                    "property float y",
+                    "property float z",
+                    "element vertex1 8",
+                    "property float x",
+                    "property double y",
+                    "property float z",
+                    "element face 6",
+                    "property list uchar uchar vertex_index",
+                    "element irregular_list 3",
+                    "property list uchar uchar vertex_index",
+                    "element mixed 2",
+                    "property list short uint foo",
+                    "property short bar",
+                    "element minus_ones 1",
+                    "property char 1",
+                    "property uchar 2",
+                    "property short 3",
+                    "property ushort 4",
+                    "property int 5",
+                    "property uint 6",
+                    "end_header\n",
+                ]
+            )
+            ply_file = b"".join(
+                [
+                    header.encode("ascii"),
+                    vertex_data,
+                    vertex1_data,
+                    face_char_data,
+                    mixed_data,
+                    minus_one_data,
+                ]
+            )
+            metadata, data = _load_ply_raw(BytesIO(ply_file))
+            self.assertFalse(metadata.ascii)
+            self.assertEqual(len(data), 6)
+            self.assertTupleEqual(data["face"].shape, (6, 4))
+            self.assertClose([0, 1, 2, 3], data["face"][0])
+            self.assertClose([3, 7, 4, 0], data["face"][5])
+
+            self.assertTupleEqual(data["vertex"].shape, (8, 3))
+            self.assertEqual(len(data["vertex1"]), 8)
+            self.assertClose(data["vertex"], data["vertex1"])
+            self.assertClose(data["vertex"].flatten(), list(map(float, verts)))
+
+            irregular = data["irregular_list"]
+            self.assertEqual(len(irregular), 3)
+            self.assertEqual(type(irregular), list)
+            [x] = irregular[0]
+            self.assertClose(x, [0, 1, 2, 3])
+            [x] = irregular[1]
+            self.assertClose(x, [7, 6, 5, 4])
+            [x] = irregular[2]
+            self.assertClose(x, [4, 5, 1])
+
+            mixed = data["mixed"]
+            self.assertEqual(len(mixed), 2)
+            self.assertEqual(len(mixed[0]), 2)
+            self.assertEqual(len(mixed[1]), 2)
+            self.assertEqual(mixed[0][1], 3 * 256 + 3)
+            self.assertEqual(len(mixed[0][0]), 0)
+            self.assertEqual(mixed[1][1], (2 if big_endian else 2 * 256))
+            base = 1 + 256 + 256 * 256
+            self.assertEqual(len(mixed[1][0]), 1)
+            self.assertEqual(mixed[1][0][0], base if big_endian else 256 * base)
+
+            self.assertListEqual(
+                data["minus_ones"], [(-1, 255, -1, 65535, -1, 4294967295)]
+            )
+
+    def test_bad_ply_syntax(self):
+        """Some syntactically bad ply files."""
+        lines = [
+            "ply",
+            "format ascii 1.0",
+            "comment dashfadskfj;k",
+            "element vertex 1",
+            "property float x",
+            "element listy 1",
+            "property list uint int x",
+            "end_header",
+            "0",
+            "0",
+        ]
+        lines2 = lines.copy()
+        # this is ok
+        _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[0] = "PLY"
+        with self.assertRaisesRegex(ValueError, "Invalid file header."):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[2] = "#this is a comment"
+        with self.assertRaisesRegex(ValueError, "Invalid line.*"):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[3] = lines[4]
+        lines2[4] = lines[3]
+        with self.assertRaisesRegex(
+            ValueError, "Encountered property before any element."
+        ):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[8] = "1 2"
+        with self.assertRaisesRegex(
+            ValueError, "Inconsistent data for vertex."
+        ):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines[:-1]
+        with self.assertRaisesRegex(ValueError, "Not enough data for listy."):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[5] = "element listy 2"
+        with self.assertRaisesRegex(ValueError, "Not enough data for listy."):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2.insert(4, "property short x")
+        with self.assertRaisesRegex(
+            ValueError, "Cannot have two properties called x in vertex."
+        ):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2.insert(4, "property zz short")
+        with self.assertRaisesRegex(ValueError, "Invalid datatype: zz"):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2.append("3")
+        with self.assertRaisesRegex(ValueError, "Extra data at end of file."):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2.append("comment foo")
+        with self.assertRaisesRegex(ValueError, "Extra data at end of file."):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2.insert(4, "element bad 1")
+        with self.assertRaisesRegex(
+            ValueError, "Found an element with no properties."
+        ):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[-1] = "3 2 3 3"
+        _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[-1] = "3 1 2 3 4"
+        msg = "A line of listy data did not have the specified length."
+        with self.assertRaisesRegex(ValueError, msg):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2 = lines.copy()
+        lines2[3] = "element vertex one"
+        msg = "Number of items for vertex was not a number."
+        with self.assertRaisesRegex(ValueError, msg):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        # Heterogenous cases
+        lines2 = lines.copy()
+        lines2.insert(4, "property double y")
+
+        with self.assertRaisesRegex(
+            ValueError, "Too little data for an element."
+        ):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2[-2] = "3.3 4.2"
+        _load_ply_raw(StringIO("\n".join(lines2)))
+
+        lines2[-2] = "3.3 4.3 2"
+        with self.assertRaisesRegex(
+            ValueError, "Too much data for an element."
+        ):
+            _load_ply_raw(StringIO("\n".join(lines2)))
+
+        # Now make the ply file actually be readable as a Mesh
+
+        with self.assertRaisesRegex(
+            ValueError, "The ply file has no face element."
+        ):
+            load_ply(StringIO("\n".join(lines)))
+
+        lines2 = lines.copy()
+        lines2[5] = "element face 1"
+        with self.assertRaisesRegex(ValueError, "Invalid vertices in file."):
+            load_ply(StringIO("\n".join(lines2)))
+
+        lines2.insert(5, "property float z")
+        lines2.insert(5, "property float y")
+        lines2[-2] = "0 0 0"
+        with self.assertRaisesRegex(
+            ValueError, "Faces must have at least 3 vertices."
+        ):
+            load_ply(StringIO("\n".join(lines2)))
+
+        # Good one
+        lines2[-1] = "3 0 0 0"
+        load_ply(StringIO("\n".join(lines2)))
+
+    @staticmethod
+    def save_ply_bm(V: int, F: int):
+        verts_list = torch.tensor(V * [[0.11, 0.22, 0.33]]).view(-1, 3)
+        faces_list = torch.tensor(F * [[0, 1, 2]]).view(-1, 3)
+
+        def save_mesh():
+            file = StringIO()
+            save_ply(file, verts_list, faces_list, 2)
+
+        return save_mesh
+
+    @staticmethod
+    def load_ply_bm(V: int, F: int):
+        verts = torch.tensor([[0.1, 0.2, 0.3]]).expand(V, 3)
+        faces = torch.tensor([[0, 1, 2]], dtype=torch.int64).expand(F, 3)
+        ply_file = StringIO()
+        save_ply(ply_file, verts=verts, faces=faces)
+        ply = ply_file.getvalue()
+        # Recreate stream so it's unaffected by how it was created.
+
+        def load_mesh():
+            ply_file = StringIO(ply)
+            verts, faces = load_ply(ply_file)
+
+        return load_mesh

tests/test_rasterize_meshes.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import functools
+import unittest
+import torch
+
+from pytorch3d import _C
+from pytorch3d.renderer.mesh.rasterize_meshes import (
+    rasterize_meshes,
+    rasterize_meshes_python,
+)
+from pytorch3d.structures import Meshes
+from pytorch3d.utils import ico_sphere
+
+
+class TestRasterizeMeshes(unittest.TestCase):
+    def test_simple_python(self):
+        device = torch.device("cpu")
+        self._simple_triangle_raster(
+            rasterize_meshes_python, device, bin_size=-1
+        )  # don't set binsize
+        self._simple_blurry_raster(rasterize_meshes_python, device, bin_size=-1)
+        self._test_behind_camera(rasterize_meshes_python, device, bin_size=-1)
+        self._test_perspective_correct(
+            rasterize_meshes_python, device, bin_size=-1
+        )
+
+    def test_simple_cpu_naive(self):
+        device = torch.device("cpu")
+        self._simple_triangle_raster(rasterize_meshes, device)
+        self._simple_blurry_raster(rasterize_meshes, device)
+        self._test_behind_camera(rasterize_meshes, device)
+        self._test_perspective_correct(rasterize_meshes, device)
+
+    def test_simple_cuda_naive(self):
+        device = torch.device("cuda:0")
+        self._simple_triangle_raster(rasterize_meshes, device, bin_size=0)
+        self._simple_blurry_raster(rasterize_meshes, device, bin_size=0)
+        self._test_behind_camera(rasterize_meshes, device, bin_size=0)
+        self._test_perspective_correct(rasterize_meshes, device, bin_size=0)
+
+    def test_simple_cuda_binned(self):
+        device = torch.device("cuda:0")
+        self._simple_triangle_raster(rasterize_meshes, device, bin_size=5)
+        self._simple_blurry_raster(rasterize_meshes, device, bin_size=5)
+        self._test_behind_camera(rasterize_meshes, device, bin_size=5)
+        self._test_perspective_correct(rasterize_meshes, device, bin_size=5)
+
+    def test_python_vs_cpu_vs_cuda(self):
+        torch.manual_seed(231)
+        device = torch.device("cpu")
+        image_size = 32
+        blur_radius = 0.1 ** 2
+        faces_per_pixel = 3
+
+        for d in ["cpu", "cuda"]:
+            device = torch.device(d)
+            compare_grads = True
+            # Mesh with a single face.
+            verts1 = torch.tensor(
+                [[0.0, 0.6, 0.1], [-0.7, -0.4, 0.5], [0.7, -0.4, 0.7]],
+                dtype=torch.float32,
+                requires_grad=True,
+                device=device,
+            )
+            faces1 = torch.tensor([[0, 1, 2]], dtype=torch.int64, device=device)
+            meshes1 = Meshes(verts=[verts1], faces=[faces1])
+            args1 = (meshes1, image_size, blur_radius, faces_per_pixel)
+            verts2 = verts1.detach().clone()
+            verts2.requires_grad = True
+            meshes2 = Meshes(verts=[verts2], faces=[faces1])
+            args2 = (meshes2, image_size, blur_radius, faces_per_pixel)
+            self._compare_impls(
+                rasterize_meshes_python,
+                rasterize_meshes,
+                args1,
+                args2,
+                verts1,
+                verts2,
+                compare_grads=compare_grads,
+            )
+
+            # Mesh with multiple faces.
+            # fmt: off
+            verts1 = torch.tensor(
+                [
+                    [ -0.5, 0.0,  0.1],  # noqa: E241, E201
+                    [  0.0, 0.6,  0.5],  # noqa: E241, E201
+                    [  0.5, 0.0,  0.7],  # noqa: E241, E201
+                    [-0.25, 0.0,  0.9],  # noqa: E241, E201
+                    [ 0.26, 0.5,  0.8],  # noqa: E241, E201
+                    [ 0.76, 0.0,  0.8],  # noqa: E241, E201
+                    [-0.41, 0.0,  0.5],  # noqa: E241, E201
+                    [ 0.61, 0.6,  0.6],  # noqa: E241, E201
+                    [ 0.41, 0.0,  0.5],  # noqa: E241, E201
+                    [ -0.2, 0.0, -0.5],  # noqa: E241, E201
+                    [  0.3, 0.6, -0.5],  # noqa: E241, E201
+                    [  0.4, 0.0, -0.5],  # noqa: E241, E201
+                ],
+                dtype=torch.float32,
+                device=device,
+                requires_grad=True
+            )
+            faces1 = torch.tensor(
+                [
+                    [ 1, 0,  2],  # noqa: E241, E201
+                    [ 4, 3,  5],  # noqa: E241, E201
+                    [ 7, 6,  8],  # noqa: E241, E201
+                    [10, 9, 11]   # noqa: E241, E201
+                ],
+                dtype=torch.int64,
+                device=device,
+            )
+            # fmt: on
+            meshes = Meshes(verts=[verts1], faces=[faces1])
+            args1 = (meshes, image_size, blur_radius, faces_per_pixel)
+            verts2 = verts1.clone().detach()
+            verts2.requires_grad = True
+            meshes2 = Meshes(verts=[verts2], faces=[faces1])
+            args2 = (meshes2, image_size, blur_radius, faces_per_pixel)
+            self._compare_impls(
+                rasterize_meshes_python,
+                rasterize_meshes,
+                args1,
+                args2,
+                verts1,
+                verts2,
+                compare_grads=compare_grads,
+            )
+
+            # Icosphere
+            meshes = ico_sphere(device=device)
+            verts1, faces1 = meshes.get_mesh_verts_faces(0)
+            verts1.requires_grad = True
+            meshes = Meshes(verts=[verts1], faces=[faces1])
+            args1 = (meshes, image_size, blur_radius, faces_per_pixel)
+            verts2 = verts1.detach().clone()
+            verts2.requires_grad = True
+            meshes2 = Meshes(verts=[verts2], faces=[faces1])
+            args2 = (meshes2, image_size, blur_radius, faces_per_pixel)
+            self._compare_impls(
+                rasterize_meshes_python,
+                rasterize_meshes,
+                args1,
+                args2,
+                verts1,
+                verts2,
+                compare_grads=compare_grads,
+            )
+
+    def test_cpu_vs_cuda_naive(self):
+        """
+        Compare naive versions of cuda and cpp
+        """
+
+        torch.manual_seed(231)
+        image_size = 64
+        radius = 0.1 ** 2
+        faces_per_pixel = 3
+        device = torch.device("cpu")
+        meshes_cpu = ico_sphere(0, device)
+        verts1, faces1 = meshes_cpu.get_mesh_verts_faces(0)
+        verts1.requires_grad = True
+        meshes_cpu = Meshes(verts=[verts1], faces=[faces1])
+
+        device = torch.device("cuda:0")
+        meshes_cuda = ico_sphere(0, device)
+        verts2, faces2 = meshes_cuda.get_mesh_verts_faces(0)
+        verts2.requires_grad = True
+        meshes_cuda = Meshes(verts=[verts2], faces=[faces2])
+
+        args_cpu = (meshes_cpu, image_size, radius, faces_per_pixel)
+        args_cuda = (meshes_cuda, image_size, radius, faces_per_pixel, 0, 0)
+        self._compare_impls(
+            rasterize_meshes,
+            rasterize_meshes,
+            args_cpu,
+            args_cuda,
+            verts1,
+            verts2,
+            compare_grads=True,
+        )
+
+    def test_coarse_cpu(self):
+        return self._test_coarse_rasterize(torch.device("cpu"))
+
+    def test_coarse_cuda(self):
+        return self._test_coarse_rasterize(torch.device("cuda:0"))
+
+    def test_cpp_vs_cuda_naive_vs_cuda_binned(self):
+        # Make sure that the backward pass runs for all pathways
+        image_size = 64  # test is too slow for very large images.
+        N = 1
+        radius = 0.1 ** 2
+        faces_per_pixel = 3
+
+        grad_zbuf = torch.randn(N, image_size, image_size, faces_per_pixel)
+        grad_dist = torch.randn(N, image_size, image_size, faces_per_pixel)
+        grad_bary = torch.randn(N, image_size, image_size, faces_per_pixel, 3)
+
+        device = torch.device("cpu")
+        meshes = ico_sphere(0, device)
+        verts, faces = meshes.get_mesh_verts_faces(0)
+        verts.requires_grad = True
+        meshes = Meshes(verts=[verts], faces=[faces])
+
+        # Option I: CPU, naive
+        args = (meshes, image_size, radius, faces_per_pixel)
+        idx1, zbuf1, bary1, dist1 = rasterize_meshes(*args)
+
+        loss = (
+            (zbuf1 * grad_zbuf).sum()
+            + (dist1 * grad_dist).sum()
+            + (bary1 * grad_bary).sum()
+        )
+        loss.backward()
+        idx1 = idx1.data.cpu().clone()
+        zbuf1 = zbuf1.data.cpu().clone()
+        dist1 = dist1.data.cpu().clone()
+        grad1 = verts.grad.data.cpu().clone()
+
+        # Option II: CUDA, naive
+        device = torch.device("cuda:0")
+        meshes = ico_sphere(0, device)
+        verts, faces = meshes.get_mesh_verts_faces(0)
+        verts.requires_grad = True
+        meshes = Meshes(verts=[verts], faces=[faces])
+
+        args = (meshes, image_size, radius, faces_per_pixel, 0, 0)
+        idx2, zbuf2, bary2, dist2 = rasterize_meshes(*args)
+        grad_zbuf = grad_zbuf.cuda()
+        grad_dist = grad_dist.cuda()
+        grad_bary = grad_bary.cuda()
+        loss = (
+            (zbuf2 * grad_zbuf).sum()
+            + (dist2 * grad_dist).sum()
+            + (bary2 * grad_bary).sum()
+        )
+        loss.backward()
+        idx2 = idx2.data.cpu().clone()
+        zbuf2 = zbuf2.data.cpu().clone()
+        dist2 = dist2.data.cpu().clone()
+        grad2 = verts.grad.data.cpu().clone()
+
+        # Option III: CUDA, binned
+        device = torch.device("cuda:0")
+        meshes = ico_sphere(0, device)
+        verts, faces = meshes.get_mesh_verts_faces(0)
+        verts.requires_grad = True
+        meshes = Meshes(verts=[verts], faces=[faces])
+
+        args = (meshes, image_size, radius, faces_per_pixel, 32, 500)
+        idx3, zbuf3, bary3, dist3 = rasterize_meshes(*args)
+
+        loss = (
+            (zbuf3 * grad_zbuf).sum()
+            + (dist3 * grad_dist).sum()
+            + (bary3 * grad_bary).sum()
+        )
+        loss.backward()
+        idx3 = idx3.data.cpu().clone()
+        zbuf3 = zbuf3.data.cpu().clone()
+        dist3 = dist3.data.cpu().clone()
+        grad3 = verts.grad.data.cpu().clone()
+
+        # Make sure everything was the same
+        self.assertTrue((idx1 == idx2).all().item())
+        self.assertTrue((idx1 == idx3).all().item())
+        self.assertTrue(torch.allclose(zbuf1, zbuf2, atol=1e-6))
+        self.assertTrue(torch.allclose(zbuf1, zbuf3, atol=1e-6))
+        self.assertTrue(torch.allclose(dist1, dist2, atol=1e-6))
+        self.assertTrue(torch.allclose(dist1, dist3, atol=1e-6))
+
+        self.assertTrue(torch.allclose(grad1, grad2, rtol=5e-3))  # flaky test
+        self.assertTrue(torch.allclose(grad1, grad3, rtol=5e-3))
+        self.assertTrue(torch.allclose(grad2, grad3, rtol=5e-3))
+
+    def test_compare_coarse_cpu_vs_cuda(self):
+        torch.manual_seed(231)
+        N = 1
+        image_size = 512
+        blur_radius = 0.0
+        bin_size = 32
+        max_faces_per_bin = 20
+
+        device = torch.device("cpu")
+        meshes = ico_sphere(2, device)
+
+        faces = meshes.faces_packed()
+        verts = meshes.verts_packed()
+        faces_verts = verts[faces]
+        num_faces_per_mesh = meshes.num_faces_per_mesh()
+        mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
+        args = (
+            faces_verts,
+            mesh_to_face_first_idx,
+            num_faces_per_mesh,
+            image_size,
+            blur_radius,
+            bin_size,
+            max_faces_per_bin,
+        )
+        bin_faces_cpu = _C._rasterize_meshes_coarse(*args)
+
+        device = torch.device("cuda:0")
+        meshes = ico_sphere(2, device)
+
+        faces = meshes.faces_packed()
+        verts = meshes.verts_packed()
+        faces_verts = verts[faces]
+        num_faces_per_mesh = meshes.num_faces_per_mesh()
+        mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
+        args = (
+            faces_verts,
+            mesh_to_face_first_idx,
+            num_faces_per_mesh,
+            image_size,
+            blur_radius,
+            bin_size,
+            max_faces_per_bin,
+        )
+        bin_faces_cuda = _C._rasterize_meshes_coarse(*args)
+
+        # Bin faces might not be the same: CUDA version might write them in
+        # any order. But if we sort the non-(-1) elements of the CUDA output
+        # then they should be the same.
+        for n in range(N):
+            for by in range(bin_faces_cpu.shape[1]):
+                for bx in range(bin_faces_cpu.shape[2]):
+                    K = (bin_faces_cuda[n, by, bx] != -1).sum().item()
+                    idxs_cpu = bin_faces_cpu[n, by, bx].tolist()
+                    idxs_cuda = bin_faces_cuda[n, by, bx].tolist()
+                    idxs_cuda[:K] = sorted(idxs_cuda[:K])
+                    self.assertEqual(idxs_cpu, idxs_cuda)
+
+    def test_python_vs_cpp_perspective_correct(self):
+        torch.manual_seed(232)
+        N = 2
+        V = 10
+        F = 5
+        verts1 = torch.randn(N, V, 3, requires_grad=True)
+        verts2 = verts1.detach().clone().requires_grad_(True)
+        faces = torch.randint(V, size=(N, F, 3))
+        meshes1 = Meshes(verts1, faces)
+        meshes2 = Meshes(verts2, faces)
+
+        kwargs = {"image_size": 24, "perspective_correct": True}
+        fn1 = functools.partial(rasterize_meshes, meshes1, **kwargs)
+        fn2 = functools.partial(rasterize_meshes_python, meshes2, **kwargs)
+        args = ()
+        self._compare_impls(
+            fn1, fn2, args, args, verts1, verts2, compare_grads=True
+        )
+
+    def test_cpp_vs_cuda_perspective_correct(self):
+        meshes = ico_sphere(2, device=torch.device("cpu"))
+        verts1, faces1 = meshes.get_mesh_verts_faces(0)
+        verts1.requires_grad = True
+        meshes1 = Meshes(verts=[verts1], faces=[faces1])
+        verts2 = verts1.detach().cuda().requires_grad_(True)
+        faces2 = faces1.detach().clone().cuda()
+        meshes2 = Meshes(verts=[verts2], faces=[faces2])
+
+        kwargs = {"image_size": 64, "perspective_correct": True}
+        fn1 = functools.partial(rasterize_meshes, meshes1, **kwargs)
+        fn2 = functools.partial(rasterize_meshes, meshes2, bin_size=0, **kwargs)
+        args = ()
+        self._compare_impls(
+            fn1, fn2, args, args, verts1, verts2, compare_grads=True
+        )
+
+    def test_cuda_naive_vs_binned_perspective_correct(self):
+        meshes = ico_sphere(2, device=torch.device("cuda"))
+        verts1, faces1 = meshes.get_mesh_verts_faces(0)
+        verts1.requires_grad = True
+        meshes1 = Meshes(verts=[verts1], faces=[faces1])
+        verts2 = verts1.detach().clone().requires_grad_(True)
+        faces2 = faces1.detach().clone()
+        meshes2 = Meshes(verts=[verts2], faces=[faces2])
+
+        kwargs = {"image_size": 64, "perspective_correct": True}
+        fn1 = functools.partial(rasterize_meshes, meshes1, bin_size=0, **kwargs)
+        fn2 = functools.partial(rasterize_meshes, meshes2, bin_size=8, **kwargs)
+        args = ()
+        self._compare_impls(
+            fn1, fn2, args, args, verts1, verts2, compare_grads=True
+        )
+
+    def _compare_impls(
+        self,
+        fn1,
+        fn2,
+        args1,
+        args2,
+        grad_var1=None,
+        grad_var2=None,
+        compare_grads=False,
+    ):
+        idx1, zbuf1, bary1, dist1 = fn1(*args1)
+        idx2, zbuf2, bary2, dist2 = fn2(*args2)
+        self.assertTrue((idx1.cpu() == idx2.cpu()).all().item())
+        self.assertTrue(torch.allclose(zbuf1.cpu(), zbuf2.cpu(), rtol=1e-4))
+        self.assertTrue(torch.allclose(dist1.cpu(), dist2.cpu(), rtol=6e-3))
+        self.assertTrue(torch.allclose(bary1.cpu(), bary2.cpu(), rtol=1e-3))
+        if not compare_grads:
+            return
+
+        # Compare gradients.
+        torch.manual_seed(231)
+        grad_zbuf = torch.randn_like(zbuf1)
+        grad_dist = torch.randn_like(dist1)
+        grad_bary = torch.randn_like(bary1)
+        loss1 = (
+            (dist1 * grad_dist).sum()
+            + (zbuf1 * grad_zbuf).sum()
+            + (bary1 * grad_bary).sum()
+        )
+        loss1.backward()
+        grad_verts1 = grad_var1.grad.data.clone().cpu()
+
+        grad_zbuf = grad_zbuf.to(zbuf2)
+        grad_dist = grad_dist.to(dist2)
+        grad_bary = grad_bary.to(bary2)
+        loss2 = (
+            (dist2 * grad_dist).sum()
+            + (zbuf2 * grad_zbuf).sum()
+            + (bary2 * grad_bary).sum()
+        )
+        grad_var1.grad.data.zero_()
+        loss2.backward()
+        grad_verts2 = grad_var2.grad.data.clone().cpu()
+        self.assertTrue(torch.allclose(grad_verts1, grad_verts2, rtol=1e-3))
+
+    def _test_perspective_correct(
+        self, rasterize_meshes_fn, device, bin_size=None
+    ):
+        # fmt: off
+        verts = torch.tensor([
+                [-0.4, -0.4, 10],  # noqa: E241, E201
+                [ 0.4, -0.4, 10],  # noqa: E241, E201
+                [ 0.0,  0.4, 20],  # noqa: E241, E201
+        ], dtype=torch.float32, device=device)
+        # fmt: on
+        faces = torch.tensor([[0, 1, 2]], device=device)
+        meshes = Meshes(verts=[verts], faces=[faces])
+        kwargs = {
+            "meshes": meshes,
+            "image_size": 11,
+            "faces_per_pixel": 1,
+            "blur_radius": 0.2,
+            "perspective_correct": False,
+        }
+        if bin_size != -1:
+            kwargs["bin_size"] = bin_size
+
+        # Run with and without perspective correction
+        idx_f, zbuf_f, bary_f, dists_f = rasterize_meshes_fn(**kwargs)
+        kwargs["perspective_correct"] = True
+        idx_t, zbuf_t, bary_t, dists_t = rasterize_meshes_fn(**kwargs)
+
+        # idx and dists should be the same with or without perspecitve correction
+        # fmt: off
+        idx_expected = torch.tensor([
+            [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+            [-1, -1,  0,  0,  0,  0,  0,  0,  0, -1, -1],  # noqa: E241, E201
+            [-1,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1],  # noqa: E241, E201
+            [-1,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1],  # noqa: E241, E201
+            [-1,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1],  # noqa: E241, E201
+            [-1, -1,  0,  0,  0,  0,  0,  0,  0, -1, -1],  # noqa: E241, E201
+            [-1, -1,  0,  0,  0,  0,  0,  0,  0, -1, -1],  # noqa: E241, E201
+            [-1, -1, -1,  0,  0,  0,  0,  0, -1, -1, -1],  # noqa: E241, E201
+            [-1, -1, -1,  0,  0,  0,  0,  0, -1, -1, -1],  # noqa: E241, E201
+            [-1, -1, -1, -1,  0,  0,  0, -1, -1, -1, -1],  # noqa: E241, E201
+            [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+        ], dtype=torch.int64, device=device).view(1, 11, 11, 1)
+        dists_expected = torch.tensor([
+            [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000,  0.1283,  0.1071,  0.1071,  0.1071,  0.1071,  0.1071,  0.1283, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000,  0.1283,  0.0423,  0.0212,  0.0212,  0.0212,  0.0212,  0.0212,  0.0423,  0.1283, -1.0000],  # noqa: E241, E201
+            [-1.0000,  0.1084,  0.0225, -0.0003, -0.0013, -0.0013, -0.0013, -0.0003,  0.0225,  0.1084, -1.0000],  # noqa: E241, E201
+            [-1.0000,  0.1523,  0.0518,  0.0042, -0.0095, -0.0476, -0.0095,  0.0042,  0.0518,  0.1523, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000,  0.0955,  0.0214, -0.0003, -0.0320, -0.0003,  0.0214,  0.0955, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000,  0.1523,  0.0518,  0.0042, -0.0095,  0.0042,  0.0518,  0.1523, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000,  0.0955,  0.0214, -0.0003,  0.0214,  0.0955, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000,  0.1523,  0.0542,  0.0212,  0.0542,  0.1523, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, -1.0000,  0.1402,  0.1071,  0.1402, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+        ], dtype=torch.float32, device=device).view(1, 11, 11, 1)
+
+        # zbuf and barycentric will be different with perspective correction
+        zbuf_f_expected = torch.tensor([
+            [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000,  5.9091,  5.9091,  5.9091,  5.9091,  5.9091,  5.9091,  5.9091, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000,  8.1818,  8.1818,  8.1818,  8.1818,  8.1818,  8.1818,  8.1818,  8.1818,  8.1818, -1.0000],  # noqa: E241, E201
+            [-1.0000, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, -1.0000],  # noqa: E241, E201
+            [-1.0000, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, 19.5455, 19.5455, 19.5455, 19.5455, 19.5455, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, 21.8182, 21.8182, 21.8182, 21.8182, 21.8182, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, -1.0000, 24.0909, 24.0909, 24.0909, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+        ], dtype=torch.float32, device=device).view(1, 11, 11, 1)
+        zbuf_t_expected = torch.tensor([
+            [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000,  8.3019,  8.3019,  8.3019,  8.3019,  8.3019,  8.3019,  8.3019, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000,  9.1667,  9.1667,  9.1667,  9.1667,  9.1667,  9.1667,  9.1667,  9.1667,  9.1667, -1.0000],  # noqa: E241, E201
+            [-1.0000, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.0000],  # noqa: E241, E201
+            [-1.0000, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, -1.0000, 33.8462, 33.8462, 33.8461, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+            [-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],  # noqa: E241, E201
+        ], dtype=torch.float32, device=device).view(1, 11, 11, 1)
+        # fmt: on
+
+        self.assertTrue(torch.all(idx_f == idx_expected).item())
+        self.assertTrue(torch.all(idx_t == idx_expected).item())
+        dists_t_max_diff = (dists_t - dists_expected).abs().max().item()
+        dists_f_max_diff = (dists_f - dists_expected).abs().max().item()
+        self.assertLess(dists_t_max_diff, 1e-4)
+        self.assertLess(dists_f_max_diff, 1e-4)
+        zbuf_f_max_diff = (zbuf_f - zbuf_f_expected).abs().max().item()
+        zbuf_t_max_diff = (zbuf_t - zbuf_t_expected).abs().max().item()
+        self.assertLess(zbuf_f_max_diff, 1e-4)
+        self.assertLess(zbuf_t_max_diff, 1e-4)
+
+        # Check barycentrics by using them to re-compute zbuf
+        z0 = verts[0, 2]
+        z1 = verts[1, 2]
+        z2 = verts[2, 2]
+        w0_f, w1_f, w2_f = bary_f.unbind(dim=4)
+        w0_t, w1_t, w2_t = bary_t.unbind(dim=4)
+        zbuf_f_bary = w0_f * z0 + w1_f * z1 + w2_f * z2
+        zbuf_t_bary = w0_t * z0 + w1_t * z1 + w2_t * z2
+        mask = idx_expected != -1
+        zbuf_f_bary_diff = (
+            (zbuf_f_bary[mask] - zbuf_f_expected[mask]).abs().max()
+        )
+        zbuf_t_bary_diff = (
+            (zbuf_t_bary[mask] - zbuf_t_expected[mask]).abs().max()
+        )
+        self.assertLess(zbuf_f_bary_diff, 1e-4)
+        self.assertLess(zbuf_t_bary_diff, 1e-4)
+
+    def _test_behind_camera(self, rasterize_meshes_fn, device, bin_size=None):
+        """
+        All verts are behind the camera so nothing should get rasterized.
+        """
+        N = 1
+        # fmt: off
+        verts = torch.tensor(
+            [
+                [ -0.5, 0.0, -0.1],  # noqa: E241, E201
+                [  0.0, 0.6, -0.1],  # noqa: E241, E201
+                [  0.5, 0.0, -0.1],  # noqa: E241, E201
+                [-0.25, 0.0, -0.9],  # noqa: E241, E201
+                [ 0.25, 0.5, -0.9],  # noqa: E241, E201
+                [ 0.75, 0.0, -0.9],  # noqa: E241, E201
+                [ -0.4, 0.0, -0.5],  # noqa: E241, E201
+                [  0.6, 0.6, -0.5],  # noqa: E241, E201
+                [  0.8, 0.0, -0.5],  # noqa: E241, E201
+                [ -0.2, 0.0, -0.5],  # noqa: E241, E201
+                [  0.3, 0.6, -0.5],  # noqa: E241, E201
+                [  0.4, 0.0, -0.5],  # noqa: E241, E201
+            ],
+            dtype=torch.float32,
+            device=device,
+        )
+        # fmt: on
+        faces = torch.tensor(
+            [[1, 0, 2], [4, 3, 5], [7, 6, 8], [10, 9, 11]],
+            dtype=torch.int64,
+            device=device,
+        )
+        meshes = Meshes(verts=[verts], faces=[faces])
+        image_size = 16
+        faces_per_pixel = 1
+        radius = 0.2
+        idx_expected = torch.full(
+            (N, image_size, image_size, faces_per_pixel),
+            fill_value=-1,
+            dtype=torch.int64,
+            device=device,
+        )
+        bary_expected = torch.full(
+            (N, image_size, image_size, faces_per_pixel, 3),
+            fill_value=-1,
+            dtype=torch.float32,
+            device=device,
+        )
+        zbuf_expected = torch.full(
+            (N, image_size, image_size, faces_per_pixel),
+            fill_value=-1,
+            dtype=torch.float32,
+            device=device,
+        )
+        dists_expected = zbuf_expected.clone()
+        if bin_size == -1:
+            # naive python version with no binning
+            idx, zbuf, bary, dists = rasterize_meshes_fn(
+                meshes, image_size, radius, faces_per_pixel
+            )
+        else:
+            idx, zbuf, bary, dists = rasterize_meshes_fn(
+                meshes, image_size, radius, faces_per_pixel, bin_size
+            )
+        idx_same = (idx == idx_expected).all().item()
+        zbuf_same = (zbuf == zbuf_expected).all().item()
+        self.assertTrue(idx_same)
+        self.assertTrue(zbuf_same)
+        self.assertTrue(torch.allclose(bary, bary_expected))
+        self.assertTrue(torch.allclose(dists, dists_expected))
+
+    def _simple_triangle_raster(self, raster_fn, device, bin_size=None):
+        image_size = 10
+
+        # Mesh with a single face.
+        verts0 = torch.tensor(
+            [[-0.7, -0.4, 0.1], [0.0, 0.6, 0.1], [0.7, -0.4, 0.1]],
+            dtype=torch.float32,
+            device=device,
+        )
+        faces0 = torch.tensor([[1, 0, 2]], dtype=torch.int64, device=device)
+
+        # Mesh with two overlapping faces.
+        # fmt: off
+        verts1 = torch.tensor(
+            [
+                [-0.7, -0.4, 0.1],  # noqa: E241, E201
+                [ 0.0,  0.6, 0.1],  # noqa: E241, E201
+                [ 0.7, -0.4, 0.1],  # noqa: E241, E201
+                [-0.7,  0.4, 0.5],  # noqa: E241, E201
+                [ 0.0, -0.6, 0.5],  # noqa: E241, E201
+                [ 0.7,  0.4, 0.5],  # noqa: E241, E201
+            ],
+            dtype=torch.float32,
+            device=device,
+        )
+        # fmt on
+        faces1 = torch.tensor(
+            [[1, 0, 2], [3, 4, 5]], dtype=torch.int64, device=device
+        )
+
+        # fmt off
+        expected_p2face_k0 = torch.tensor(
+            [
+                [
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  0,  0,  0,  0,  0,  0, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1,  0,  0,  0,  0, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1,  0,  0,  0,  0, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1,  0,  0, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                ],
+                [
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  1,  1,  1,  1,  1,  1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1,  1,  1,  1,  1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1,  1,  1,  1,  1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  2,  2,  1,  1,  2,  2, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                ],
+            ],
+            dtype=torch.int64,
+            device=device,
+        )
+        expected_zbuf_k0 = torch.tensor(
+            [
+                [
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1, 0.1, 0.1, 0.1, 0.1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1, 0.1, 0.1, 0.1, 0.1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1, 0.1, 0.1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                ],
+                [
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1, 0.1, 0.1, 0.1, 0.1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1, 0.1, 0.1, 0.1, 0.1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1, 0.5, 0.5, 0.1, 0.1, 0.5, 0.5, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                    [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1, -1, -1],  # noqa: E241, E201
+                ],
+            ],
+            device=device,
+        )
+        # fmt: on
+
+        meshes = Meshes(verts=[verts0, verts1], faces=[faces0, faces1])
+        if bin_size == -1:
+            # simple python case with no binning
+            p2face, zbuf, bary, pix_dists = raster_fn(
+                meshes, image_size, 0.0, 2
+            )
+        else:
+            p2face, zbuf, bary, pix_dists = raster_fn(
+                meshes, image_size, 0.0, 2, bin_size
+            )
+        # k = 0, closest point.
+        self.assertTrue(torch.allclose(p2face[..., 0], expected_p2face_k0))
+        self.assertTrue(torch.allclose(zbuf[..., 0], expected_zbuf_k0))
+
+        # k = 1, second closest point.
+        expected_p2face_k1 = expected_p2face_k0.clone()
+        expected_p2face_k1[0, :] = (
+            torch.ones_like(expected_p2face_k1[0, :]) * -1
+        )
+
+        # fmt: off
+        expected_p2face_k1[1, :] = torch.tensor(
+            [
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1,  2,  2, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  2,  2,  2,  2, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  2,  2,  2,  2, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1,  2,  2, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+            ],
+            dtype=torch.int64,
+            device=device,
+        )
+        expected_zbuf_k1 = expected_zbuf_k0.clone()
+        expected_zbuf_k1[0, :] = torch.ones_like(expected_zbuf_k1[0, :]) * -1
+        expected_zbuf_k1[1, :] = torch.tensor(
+            [
+                [-1, -1, -1,  -1,  -1,  -1,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1,  -1,  -1,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1,  -1,  -1,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1, 0.5, 0.5,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, 0.5, 0.5, 0.5, 0.5, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, 0.5, 0.5, 0.5, 0.5, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1, 0.5, 0.5,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1,  -1,  -1,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1,  -1,  -1,  -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1,  -1,  -1,  -1,  -1, -1, -1, -1],  # noqa: E241, E201
+            ],
+            dtype=torch.float32,
+            device=device,
+        )
+        # fmt: on
+        self.assertTrue(torch.allclose(p2face[..., 1], expected_p2face_k1))
+        self.assertTrue(torch.allclose(zbuf[..., 1], expected_zbuf_k1))
+
+    def _simple_blurry_raster(self, raster_fn, device, bin_size=None):
+        """
+        Check that pix_to_face, dist and zbuf values are invariant to the
+        ordering of faces.
+        """
+        image_size = 10
+        blur_radius = 0.12 ** 2
+        faces_per_pixel = 1
+
+        # fmt: off
+        verts = torch.tensor(
+            [
+                [ -0.5, 0.0,  0.1],  # noqa: E241, E201
+                [  0.0, 0.6,  0.1],  # noqa: E241, E201
+                [  0.5, 0.0,  0.1],  # noqa: E241, E201
+                [-0.25, 0.0,  0.9],  # noqa: E241, E201
+                [0.25,  0.5,  0.9],  # noqa: E241, E201
+                [0.75,  0.0,  0.9],  # noqa: E241, E201
+                [-0.4,  0.0,  0.5],  # noqa: E241, E201
+                [ 0.6,  0.6,  0.5],  # noqa: E241, E201
+                [ 0.8,  0.0,  0.5],  # noqa: E241, E201
+                [-0.2,  0.0, -0.5],  # noqa: E241, E201  face behind the camera
+                [ 0.3,  0.6, -0.5],  # noqa: E241, E201
+                [ 0.4,  0.0, -0.5],  # noqa: E241, E201
+            ],
+            dtype=torch.float32,
+            device=device,
+        )
+        faces_packed = torch.tensor(
+            [[1, 0, 2], [4, 3, 5], [7, 6, 8], [10, 9, 11]],
+            dtype=torch.int64,
+            device=device,
+        )
+        expected_p2f = torch.tensor(
+            [
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1,  0,  0,  0,  0,  0,  0,  2, -1],  # noqa: E241, E201
+                [-1, -1,  0,  0,  0,  0,  0,  0,  2, -1],  # noqa: E241, E201
+                [-1, -1, -1,  0,  0,  0,  0,  2,  2, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1,  0,  0,  2,  2,  2, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],  # noqa: E241, E201
+            ],
+            dtype=torch.int64,
+            device=device,
+        )
+        expected_zbuf = torch.tensor(
+            [
+                [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1,  -1, -1],  # noqa: E241, E201
+                [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1,  -1, -1],  # noqa: E241, E201
+                [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1,  -1, -1],  # noqa: E241, E201
+                [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1,  -1, -1],  # noqa: E241, E201
+                [-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, -1],  # noqa: E241, E201
+                [-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, -1],  # noqa: E241, E201
+                [-1, -1,  -1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.5, -1],  # noqa: E241, E201
+                [-1, -1,  -1,  -1, 0.1, 0.1, 0.5, 0.5, 0.5, -1],  # noqa: E241, E201
+                [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1,  -1, -1],  # noqa: E241, E201
+                [-1, -1,  -1,  -1,  -1,  -1,  -1,  -1,  -1, -1],  # noqa: E241, E201
+            ],
+            dtype=torch.float32,
+            device=device,
+        )
+        # fmt: on
+
+        for i, order in enumerate([[0, 1, 2], [1, 2, 0], [2, 0, 1]]):
+            faces = faces_packed[order]  # rearrange order of faces.
+            mesh = Meshes(verts=[verts], faces=[faces])
+            if bin_size == -1:
+                # simple python case with no binning
+                pix_to_face, zbuf, bary_coords, dists = raster_fn(
+                    mesh, image_size, blur_radius, faces_per_pixel
+                )
+            else:
+                pix_to_face, zbuf, bary_coords, dists = raster_fn(
+                    mesh, image_size, blur_radius, faces_per_pixel, bin_size
+                )
+
+            if i == 0:
+                expected_dists = dists
+            p2f = expected_p2f.clone()
+            p2f[expected_p2f == 0] = order.index(0)
+            p2f[expected_p2f == 1] = order.index(1)
+            p2f[expected_p2f == 2] = order.index(2)
+
+            self.assertTrue(torch.allclose(pix_to_face.squeeze(), p2f))
+            self.assertTrue(
+                torch.allclose(zbuf.squeeze(), expected_zbuf, rtol=1e-5)
+            )
+            self.assertTrue(torch.allclose(dists, expected_dists))
+
+    def _test_coarse_rasterize(self, device):
+        image_size = 16
+        blur_radius = 0.2 ** 2
+        bin_size = 8
+        max_faces_per_bin = 3
+
+        # fmt: off
+        verts = torch.tensor(
+            [
+                [-0.5,  0.0,  0.1],  # noqa: E241, E201
+                [ 0.0,  0.6,  0.1],  # noqa: E241, E201
+                [ 0.5,  0.0,  0.1],  # noqa: E241, E201
+                [-0.3,  0.0,  0.4],  # noqa: E241, E201
+                [ 0.3,  0.5,  0.4],  # noqa: E241, E201
+                [0.75,  0.0,  0.4],  # noqa: E241, E201
+                [-0.4, -0.3,  0.9],  # noqa: E241, E201
+                [ 0.2, -0.7,  0.9],  # noqa: E241, E201
+                [ 0.4, -0.3,  0.9],  # noqa: E241, E201
+                [-0.4,  0.0, -1.5],  # noqa: E241, E201
+                [ 0.6,  0.6, -1.5],  # noqa: E241, E201
+                [ 0.8,  0.0, -1.5],  # noqa: E241, E201
+            ],
+            device=device,
+        )
+        faces = torch.tensor(
+            [
+                [ 1, 0,  2],  # noqa: E241, E201  bin 00 and bin 01
+                [ 4, 3,  5],  # noqa: E241, E201  bin 00 and bin 01
+                [ 7, 6,  8],  # noqa: E241, E201  bin 10 and bin 11
+                [10, 9, 11],  # noqa: E241, E201  negative z, should not appear.
+            ],
+            dtype=torch.int64,
+            device=device,
+        )
+        # fmt: on
+
+        meshes = Meshes(verts=[verts], faces=[faces])
+        faces_verts = verts[faces]
+        num_faces_per_mesh = meshes.num_faces_per_mesh()
+        mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
+
+        bin_faces_expected = (
+            torch.ones(
+                (1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device
+            )
+            * -1
+        )
+        bin_faces_expected[0, 0, 0, 0:2] = torch.tensor([0, 1])
+        bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([0, 1])
+        bin_faces_expected[0, 1, 0, 0:3] = torch.tensor([0, 1, 2])
+        bin_faces_expected[0, 1, 1, 0:3] = torch.tensor([0, 1, 2])
+        bin_faces = _C._rasterize_meshes_coarse(
+            faces_verts,
+            mesh_to_face_first_idx,
+            num_faces_per_mesh,
+            image_size,
+            blur_radius,
+            bin_size,
+            max_faces_per_bin,
+        )
+        bin_faces_same = (
+            bin_faces.squeeze().flip(dims=[0]) == bin_faces_expected
+        ).all()
+        self.assertTrue(bin_faces_same.item() == 1)
+
+    @staticmethod
+    def rasterize_meshes_python_with_init(
+        num_meshes: int, ico_level: int, image_size: int, blur_radius: float
+    ):
+        device = torch.device("cpu")
+        meshes = ico_sphere(ico_level, device)
+        meshes_batch = meshes.extend(num_meshes)
+
+        def rasterize():
+            rasterize_meshes_python(meshes_batch, image_size, blur_radius)
+
+        return rasterize
+
+    @staticmethod
+    def rasterize_meshes_cpu_with_init(
+        num_meshes: int, ico_level: int, image_size: int, blur_radius: float
+    ):
+        meshes = ico_sphere(ico_level, torch.device("cpu"))
+        meshes_batch = meshes.extend(num_meshes)
+
+        def rasterize():
+            rasterize_meshes(meshes_batch, image_size, blur_radius, bin_size=0)
+
+        return rasterize
+
+    @staticmethod
+    def rasterize_meshes_cuda_with_init(
+        num_meshes: int,
+        ico_level: int,
+        image_size: int,
+        blur_radius: float,
+        bin_size: int,
+        max_faces_per_bin: int,
+    ):
+
+        meshes = ico_sphere(ico_level, torch.device("cuda:0"))
+        meshes_batch = meshes.extend(num_meshes)
+        torch.cuda.synchronize()
+
+        def rasterize():
+            rasterize_meshes(
+                meshes_batch,
+                image_size,
+                blur_radius,
+                8,
+                bin_size,
+                max_faces_per_bin,
+            )
+            torch.cuda.synchronize()
+
+        return rasterize

tests/test_rasterizer.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+import numpy as np
+import unittest
+from pathlib import Path
+import torch
+from PIL import Image
+
+from pytorch3d.renderer.cameras import (
+    OpenGLPerspectiveCameras,
+    look_at_view_transform,
+)
+from pytorch3d.renderer.mesh.rasterizer import (
+    MeshRasterizer,
+    RasterizationSettings,
+)
+from pytorch3d.utils.ico_sphere import ico_sphere
+
+DATA_DIR = Path(__file__).resolve().parent / "data"
+DEBUG = False  # Set DEBUG to true to save outputs from the tests.
+
+
+def convert_image_to_binary_mask(filename):
+    with Image.open(filename) as raw_image:
+        image = torch.from_numpy(np.array(raw_image))
+    min = image.min()
+    max = image.max()
+    image_norm = (image - min) / (max - min)
+    image_norm[image_norm > 0] == 1.0
+    image_norm = image_norm.to(torch.int64)
+    return image_norm
+
+
+class TestMeshRasterizer(unittest.TestCase):
+    def test_simple_sphere(self):
+        device = torch.device("cuda:0")
+        ref_filename = "test_rasterized_sphere.png"
+        image_ref_filename = DATA_DIR / ref_filename
+
+        # Rescale image_ref to the 0 - 1 range and convert to a binary mask.
+        image_ref = convert_image_to_binary_mask(image_ref_filename)
+
+        # Init mesh
+        sphere_mesh = ico_sphere(5, device)
+
+        # Init rasterizer settings
+        R, T = look_at_view_transform(2.7, 0, 0)
+        cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
+        raster_settings = RasterizationSettings(
+            image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
+        )
+
+        # Init rasterizer
+        rasterizer = MeshRasterizer(
+            cameras=cameras, raster_settings=raster_settings
+        )
+
+        ####################################
+        # 1. Test rasterizing a single mesh
+        ####################################
+
+        fragments = rasterizer(sphere_mesh)
+        image = fragments.pix_to_face[0, ..., 0].squeeze().cpu()
+        # Convert pix_to_face to a binary mask
+        image[image >= 0] = 1.0
+        image[image < 0] = 0.0
+
+        if DEBUG:
+            Image.fromarray((image.numpy() * 255).astype(np.uint8)).save(
+                DATA_DIR / "DEBUG_test_rasterized_sphere.png"
+            )
+
+        self.assertTrue(torch.allclose(image, image_ref))
+
+        ##################################
+        #  2. Test with a batch of meshes
+        ##################################
+
+        batch_size = 10
+        sphere_meshes = sphere_mesh.extend(batch_size)
+        fragments = rasterizer(sphere_meshes)
+        for i in range(batch_size):
+            image = fragments.pix_to_face[i, ..., 0].squeeze().cpu()
+            image[image >= 0] = 1.0
+            image[image < 0] = 0.0
+            self.assertTrue(torch.allclose(image, image_ref))
+
+        ####################################################
+        #  3. Test that passing kwargs to rasterizer works.
+        ####################################################
+
+        #  Change the view transform to zoom in.
+        R, T = look_at_view_transform(2.0, 0, 0, device=device)
+        fragments = rasterizer(sphere_mesh, R=R, T=T)
+        image = fragments.pix_to_face[0, ..., 0].squeeze().cpu()
+        image[image >= 0] = 1.0
+        image[image < 0] = 0.0
+
+        ref_filename = "test_rasterized_sphere_zoom.png"
+        image_ref_filename = DATA_DIR / ref_filename
+        image_ref = convert_image_to_binary_mask(image_ref_filename)
+
+        if DEBUG:
+            Image.fromarray((image.numpy() * 255).astype(np.uint8)).save(
+                DATA_DIR / "DEBUG_test_rasterized_sphere_zoom.png"
+            )
+        self.assertTrue(torch.allclose(image, image_ref))