camera refactoring

Summary:
Refactor cameras
* CamerasBase was enhanced with `transform_points_screen` that transforms projected points from NDC to screen space
* OpenGLPerspective, OpenGLOrthographic -> FoVPerspective, FoVOrthographic
* SfMPerspective, SfMOrthographic -> Perspective, Orthographic
* PerspectiveCamera can optionally be constructred with screen space parameters
* Note on Cameras and coordinate systems was added

Reviewed By: nikhilaravi

Differential Revision: D23168525

fbshipit-source-id: dd138e2b2cc7e0e0d9f34c45b8251c01266a2063

tests/test_cameras.py

@@ -31,12 +31,16 @@ import unittest
 import numpy as np
 import torch
 from common_testing import TestCaseMixin
+from pytorch3d.renderer.cameras import OpenGLOrthographicCameras  # deprecated
+from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras  # deprecated
+from pytorch3d.renderer.cameras import SfMOrthographicCameras  # deprecated
+from pytorch3d.renderer.cameras import SfMPerspectiveCameras  # deprecated
 from pytorch3d.renderer.cameras import (
     CamerasBase,
-    OpenGLOrthographicCameras,
-    OpenGLPerspectiveCameras,
-    SfMOrthographicCameras,
-    SfMPerspectiveCameras,
+    FoVOrthographicCameras,
+    FoVPerspectiveCameras,
+    OrthographicCameras,
+    PerspectiveCameras,
     camera_position_from_spherical_angles,
     get_world_to_view_transform,
     look_at_rotation,
@@ -109,6 +113,25 @@ def orthographic_project_naive(points, scale_xyz=(1.0, 1.0, 1.0)):
     return points
 
 
+def ndc_to_screen_points_naive(points, imsize):
+    """
+    Transforms points from PyTorch3D's NDC space to screen space
+    Args:
+        points: (N, V, 3) representing padded points
+        imsize: (N, 2) image size = (width, height)
+    Returns:
+        (N, V, 3) tensor of transformed points
+    """
+    imwidth, imheight = imsize.unbind(1)
+    imwidth = imwidth.view(-1, 1)
+    imheight = imheight.view(-1, 1)
+
+    x, y, z = points.unbind(2)
+    x = (1.0 - x) * (imwidth - 1) / 2.0
+    y = (1.0 - y) * (imheight - 1) / 2.0
+    return torch.stack((x, y, z), dim=2)
+
+
 class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
     def setUp(self) -> None:
         super().setUp()
@@ -359,6 +382,10 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
             OpenGLOrthographicCameras,
             SfMOrthographicCameras,
             SfMPerspectiveCameras,
+            FoVOrthographicCameras,
+            FoVPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
         ):
             cam = cam_type(R=R, T=T)
             RT_class = cam.get_world_to_view_transform()
@@ -374,6 +401,10 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
             OpenGLOrthographicCameras,
             SfMOrthographicCameras,
             SfMPerspectiveCameras,
+            FoVOrthographicCameras,
+            FoVPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
         ):
             cam = cam_type(R=R, T=T)
             C = cam.get_camera_center()
@@ -398,13 +429,53 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
                 cam_params["bottom"] = -(torch.rand(batch_size)) * 0.2 - 0.9
                 cam_params["left"] = -(torch.rand(batch_size)) * 0.2 - 0.9
                 cam_params["right"] = torch.rand(batch_size) * 0.2 + 0.9
-        elif cam_type in (SfMOrthographicCameras, SfMPerspectiveCameras):
+        elif cam_type in (FoVPerspectiveCameras, FoVOrthographicCameras):
+            cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
+            cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
+            if cam_type == FoVPerspectiveCameras:
+                cam_params["fov"] = torch.rand(batch_size) * 60 + 30
+                cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
+            else:
+                cam_params["max_y"] = torch.rand(batch_size) * 0.2 + 0.9
+                cam_params["min_y"] = -(torch.rand(batch_size)) * 0.2 - 0.9
+                cam_params["min_x"] = -(torch.rand(batch_size)) * 0.2 - 0.9
+                cam_params["max_x"] = torch.rand(batch_size) * 0.2 + 0.9
+        elif cam_type in (
+            SfMOrthographicCameras,
+            SfMPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
+        ):
             cam_params["focal_length"] = torch.rand(batch_size) * 10 + 0.1
             cam_params["principal_point"] = torch.randn((batch_size, 2))
+
         else:
             raise ValueError(str(cam_type))
         return cam_type(**cam_params)
 
+    @staticmethod
+    def init_equiv_cameras_ndc_screen(cam_type: CamerasBase, batch_size: int):
+        T = torch.randn(batch_size, 3) * 0.03
+        T[:, 2] = 4
+        R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
+        screen_cam_params = {"R": R, "T": T}
+        ndc_cam_params = {"R": R, "T": T}
+        if cam_type in (OrthographicCameras, PerspectiveCameras):
+            ndc_cam_params["focal_length"] = torch.rand((batch_size, 2)) * 3.0
+            ndc_cam_params["principal_point"] = torch.randn((batch_size, 2))
+
+            image_size = torch.randint(low=2, high=64, size=(batch_size, 2))
+            screen_cam_params["image_size"] = image_size
+            screen_cam_params["focal_length"] = (
+                ndc_cam_params["focal_length"] * image_size / 2.0
+            )
+            screen_cam_params["principal_point"] = (
+                (1.0 - ndc_cam_params["principal_point"]) * image_size / 2.0
+            )
+        else:
+            raise ValueError(str(cam_type))
+        return cam_type(**ndc_cam_params), cam_type(**screen_cam_params)
+
     def test_unproject_points(self, batch_size=50, num_points=100):
         """
         Checks that an unprojection of a randomly projected point cloud
@@ -416,6 +487,10 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
             OpenGLPerspectiveCameras,
             OpenGLOrthographicCameras,
             SfMPerspectiveCameras,
+            FoVOrthographicCameras,
+            FoVPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
         ):
             # init the cameras
             cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
@@ -437,9 +512,14 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
                 else:
                     matching_xyz = xyz_cam
 
-                # if we have OpenGL cameras
+                # if we have FoV (= OpenGL) cameras
                 # test for scaled_depth_input=True/False
-                if cam_type in (OpenGLPerspectiveCameras, OpenGLOrthographicCameras):
+                if cam_type in (
+                    OpenGLPerspectiveCameras,
+                    OpenGLOrthographicCameras,
+                    FoVPerspectiveCameras,
+                    FoVOrthographicCameras,
+                ):
                     for scaled_depth_input in (True, False):
                         if scaled_depth_input:
                             xy_depth_ = xyz_proj
@@ -459,6 +539,56 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
                     )
                     self.assertTrue(torch.allclose(xyz_unproj, matching_xyz, atol=1e-4))
 
+    def test_project_points_screen(self, batch_size=50, num_points=100):
+        """
+        Checks that an unprojection of a randomly projected point cloud
+        stays the same.
+        """
+
+        for cam_type in (
+            OpenGLOrthographicCameras,
+            OpenGLPerspectiveCameras,
+            SfMOrthographicCameras,
+            SfMPerspectiveCameras,
+            FoVOrthographicCameras,
+            FoVPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
+        ):
+
+            # init the cameras
+            cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
+            # xyz - the ground truth point cloud
+            xyz = torch.randn(batch_size, num_points, 3) * 0.3
+            # image size
+            image_size = torch.randint(low=2, high=64, size=(batch_size, 2))
+            # project points
+            xyz_project_ndc = cameras.transform_points(xyz)
+            xyz_project_screen = cameras.transform_points_screen(xyz, image_size)
+            # naive
+            xyz_project_screen_naive = ndc_to_screen_points_naive(
+                xyz_project_ndc, image_size
+            )
+            self.assertClose(xyz_project_screen, xyz_project_screen_naive)
+
+    def test_equiv_project_points(self, batch_size=50, num_points=100):
+        """
+        Checks that NDC and screen cameras project points to ndc correctly.
+        Applies only to OrthographicCameras and PerspectiveCameras.
+        """
+        for cam_type in (OrthographicCameras, PerspectiveCameras):
+            # init the cameras
+            (
+                ndc_cameras,
+                screen_cameras,
+            ) = TestCamerasCommon.init_equiv_cameras_ndc_screen(cam_type, batch_size)
+            # xyz - the ground truth point cloud
+            xyz = torch.randn(batch_size, num_points, 3) * 0.3
+            # project points
+            xyz_ndc_cam = ndc_cameras.transform_points(xyz)
+            xyz_screen_cam = screen_cameras.transform_points(xyz)
+            self.assertClose(xyz_ndc_cam, xyz_screen_cam, atol=1e-6)
+
     def test_clone(self, batch_size: int = 10):
         """
         Checks the clone function of the cameras.
@@ -468,6 +598,10 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
             OpenGLPerspectiveCameras,
             OpenGLOrthographicCameras,
             SfMPerspectiveCameras,
+            FoVOrthographicCameras,
+            FoVPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
         ):
             cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
             cameras = cameras.to(torch.device("cpu"))
@@ -483,11 +617,16 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
                     self.assertTrue(val == val_clone)
 
 
-class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
+############################################################
+#                FoVPerspective Camera                     #
+############################################################
+
+
+class TestFoVPerspectiveProjection(TestCaseMixin, unittest.TestCase):
     def test_perspective(self):
         far = 10.0
         near = 1.0
-        cameras = OpenGLPerspectiveCameras(znear=near, zfar=far, fov=60.0)
+        cameras = FoVPerspectiveCameras(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)
@@ -512,7 +651,7 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(v1.squeeze(), projected_verts)
 
     def test_perspective_kwargs(self):
-        cameras = OpenGLPerspectiveCameras(znear=5.0, zfar=100.0, fov=0.0)
+        cameras = FoVPerspectiveCameras(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)
@@ -528,7 +667,7 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         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)
+        cameras = FoVPerspectiveCameras(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
@@ -550,7 +689,7 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         far = torch.tensor([10.0])
         near = 1.0
         fov = torch.tensor(60.0, requires_grad=True)
-        cameras = OpenGLPerspectiveCameras(znear=near, zfar=far, fov=fov)
+        cameras = FoVPerspectiveCameras(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, :]
@@ -566,7 +705,7 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
 
     def test_camera_class_init(self):
         device = torch.device("cuda:0")
-        cam = OpenGLPerspectiveCameras(znear=10.0, zfar=(100.0, 200.0))
+        cam = FoVPerspectiveCameras(znear=10.0, zfar=(100.0, 200.0))
 
         # Check broadcasting
         self.assertTrue(cam.znear.shape == (2,))
@@ -585,7 +724,7 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         self.assertTrue(new_cam.device == device)
 
     def test_get_full_transform(self):
-        cam = OpenGLPerspectiveCameras()
+        cam = FoVPerspectiveCameras()
         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)
@@ -597,7 +736,7 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         # 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)
+        cam = FoVPerspectiveCameras(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(
@@ -608,11 +747,16 @@ class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(new_points, projected_points)
 
 
-class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
+############################################################
+#                FoVOrthographic Camera                    #
+############################################################
+
+
+class TestFoVOrthographicProjection(TestCaseMixin, unittest.TestCase):
     def test_orthographic(self):
         far = 10.0
         near = 1.0
-        cameras = OpenGLOrthographicCameras(znear=near, zfar=far)
+        cameras = FoVOrthographicCameras(znear=near, zfar=far)
         P = cameras.get_projection_transform()
 
         vertices = torch.tensor([1, 2, far], dtype=torch.float32)
@@ -637,7 +781,7 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
         # 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)
+        cameras = FoVOrthographicCameras(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)
@@ -645,7 +789,7 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(v1, projected_verts[None, None])
 
     def test_orthographic_kwargs(self):
-        cameras = OpenGLOrthographicCameras(znear=5.0, zfar=100.0)
+        cameras = FoVOrthographicCameras(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)
@@ -657,7 +801,7 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
     def test_orthographic_mixed_inputs_broadcast(self):
         far = torch.tensor([10.0, 20.0])
         near = 1.0
-        cameras = OpenGLOrthographicCameras(znear=near, zfar=far)
+        cameras = FoVOrthographicCameras(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)
@@ -674,7 +818,7 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
         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)
+        cameras = FoVOrthographicCameras(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, :]
@@ -694,9 +838,14 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(scale_grad, grad_scale)
 
 
-class TestSfMOrthographicProjection(TestCaseMixin, unittest.TestCase):
+############################################################
+#                Orthographic Camera                       #
+############################################################
+
+
+class TestOrthographicProjection(TestCaseMixin, unittest.TestCase):
     def test_orthographic(self):
-        cameras = SfMOrthographicCameras()
+        cameras = OrthographicCameras()
         P = cameras.get_projection_transform()
 
         vertices = torch.randn([3, 4, 3], dtype=torch.float32)
@@ -711,9 +860,7 @@ class TestSfMOrthographicProjection(TestCaseMixin, unittest.TestCase):
         focal_length_x = 10.0
         focal_length_y = 15.0
 
-        cameras = SfMOrthographicCameras(
-            focal_length=((focal_length_x, focal_length_y),)
-        )
+        cameras = OrthographicCameras(focal_length=((focal_length_x, focal_length_y),))
         P = cameras.get_projection_transform()
 
         vertices = torch.randn([3, 4, 3], dtype=torch.float32)
@@ -730,9 +877,7 @@ class TestSfMOrthographicProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(v1, projected_verts)
 
     def test_orthographic_kwargs(self):
-        cameras = SfMOrthographicCameras(
-            focal_length=5.0, principal_point=((2.5, 2.5),)
-        )
+        cameras = OrthographicCameras(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),)
         )
@@ -745,9 +890,14 @@ class TestSfMOrthographicProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(v1, projected_verts)
 
 
-class TestSfMPerspectiveProjection(TestCaseMixin, unittest.TestCase):
+############################################################
+#                Perspective Camera                        #
+############################################################
+
+
+class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
     def test_perspective(self):
-        cameras = SfMPerspectiveCameras()
+        cameras = PerspectiveCameras()
         P = cameras.get_projection_transform()
 
         vertices = torch.randn([3, 4, 3], dtype=torch.float32)
@@ -761,7 +911,7 @@ class TestSfMPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         p0x = 15.0
         p0y = 30.0
 
-        cameras = SfMPerspectiveCameras(
+        cameras = PerspectiveCameras(
             focal_length=((focal_length_x, focal_length_y),),
             principal_point=((p0x, p0y),),
         )
@@ -777,7 +927,7 @@ class TestSfMPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(v3[..., :2], v2[..., :2])
 
     def test_perspective_kwargs(self):
-        cameras = SfMPerspectiveCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
+        cameras = PerspectiveCameras(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),)
         )