Update cameras to accept projection matrix as input

Summary: To initialize the Cameras class currently we require the principal point, focal length and other parameters to be specified from which we calculate the intrinsic matrix. In some cases the matrix might be directly available e.g. from a dataset and the associated metadata for an image.

Reviewed By: nikhilaravi

Differential Revision: D24489509

fbshipit-source-id: 1b411f19c5f6c8074bcfbf613f3339d5e242c119

pytorch3d/renderer/utils.py

@@ -106,7 +106,7 @@ class TensorProperties(nn.Module):
             # set as attributes anything else e.g. strings, bools
             args_to_broadcast = {}
             for k, v in kwargs.items():
-                if isinstance(v, (str, bool)):
+                if v is None or isinstance(v, (str, bool)):
                     setattr(self, k, v)
                 elif isinstance(v, BROADCAST_TYPES):
                     args_to_broadcast[k] = v

tests/test_cameras.py

@@ -449,6 +449,20 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
 
 
 class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
+    def test_K(self, batch_size=10):
+        T = torch.randn(batch_size, 3)
+        R = random_rotations(batch_size)
+        K = torch.randn(batch_size, 4, 4)
+        for cam_type in (
+            FoVOrthographicCameras,
+            FoVPerspectiveCameras,
+            OrthographicCameras,
+            PerspectiveCameras,
+        ):
+            cam = cam_type(R=R, T=T, K=K)
+            cam.get_projection_transform()
+            # Just checking that we don't crash or anything
+
     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)