screen cameras lose -1

Summary:
All the renderers in PyTorch3D (pointclouds including pulsar, meshes, raysampling) use align_corners=False style. NDC space goes between the edges of the outer pixels. For a non square image with W>H, the vertical NDC space goes from -1 to 1 and the horizontal from -W/H to W/H.

However it was recently pointed out that functionality which deals with screen space inside the camera classes is inconsistent with this. It unintentionally uses align_corners=True. This fixes that.

This would change behaviour of the following:
- If you create a camera in screen coordinates, i.e. setting in_ndc=False, then anything you do with the camera which touches NDC space may be affected, including trying to use renderers. The transform_points_screen function will not be affected...
- If you call the function “transform_points_screen” on a camera defined in NDC space results will be different. I have illustrated in the diff how to get the old results from the new results but this probably isn’t the right long-term solution..

Reviewed By: gkioxari

Differential Revision: D32536305

fbshipit-source-id: 377325a9137282971dcb7ca11a6cba3fc700c9ce

docs/notes/cameras.md

@@ -16,10 +16,11 @@ This is the system that has its origin on the image plane and the `Z`-axis perpe
 This is the normalized coordinate system that confines in a volume the rendered part of the object/scene. Also known as view volume. For square images, under the PyTorch3D convention, `(+1, +1, znear)` is the top left near corner, and `(-1, -1, zfar)` is the bottom right far corner of the volume. For non-square images, the side of the volume in `XY` with the smallest length ranges from `[-1, 1]` while the larger side from `[-s, s]`, where `s` is the aspect ratio and `s > 1` (larger divided by smaller side).
 The transformation from view to NDC happens after applying the camera projection matrix (`P`).
 * **Screen coordinate system**
-This is another representation of the view volume with the `XY` coordinates defined in pixel space instead of a normalized space.
+This is another representation of the view volume with the `XY` coordinates defined in pixel space instead of a normalized space. (0,0) is the top left corner of the top left pixel
+and (W,H) is the bottom right corner of the bottom right pixel.
 
 An illustration of the 4 coordinate systems is shown below
-![cameras](https://user-images.githubusercontent.com/4369065/90317960-d9b8db80-dee1-11ea-8088-39c414b1e2fa.png)
+![cameras](https://user-images.githubusercontent.com/669761/145090051-67b506d7-6d73-4826-a677-5873b7cb92ba.png)
 
 ## Defining Cameras in PyTorch3D
 
@@ -83,18 +84,18 @@ cameras_ndc = PerspectiveCameras(focal_length=fcl_ndc, principal_point=prp_ndc)
 
 # Screen space camera
 image_size = ((128, 256),)    # (h, w)
-fcl_screen = (76.2,)          # fcl_ndc * (min(image_size) - 1) / 2
-prp_screen = ((114.8, 31.75), )  # (w - 1) / 2 - px_ndc * (min(image_size) - 1) / 2, (h - 1) / 2 - py_ndc * (min(image_size) - 1) / 2
+fcl_screen = (76.8,)          # fcl_ndc * min(image_size) / 2
+prp_screen = ((115.2, 48), )  # w / 2 - px_ndc * min(image_size) / 2, h / 2 - py_ndc * min(image_size) / 2
 cameras_screen = PerspectiveCameras(focal_length=fcl_screen, principal_point=prp_screen, in_ndc=False, image_size=image_size)
 ```
 
 The relationship between screen and NDC specifications of a camera's `focal_length` and `principal_point` is given by the following equations, where `s = min(image_width, image_height)`.
 The transformation of x and y coordinates between screen and NDC is exactly the same as for px and py.
 
 ```
-fx_ndc = fx_screen * 2.0 / (s - 1)
-fy_ndc = fy_screen * 2.0 / (s - 1)
+fx_ndc = fx_screen * 2.0 / s
+fy_ndc = fy_screen * 2.0 / s
 
-px_ndc = - (px_screen - (image_width - 1) / 2.0) * 2.0 / (s - 1)
-py_ndc = - (py_screen - (image_height - 1) / 2.0) * 2.0 / (s - 1)
+px_ndc = - (px_screen - image_width / 2.0) * 2.0 / s
+py_ndc = - (py_screen - image_height / 2.0) * 2.0 / s
 ```

pytorch3d/renderer/camera_conversions.py

@@ -33,9 +33,9 @@ def _cameras_from_opencv_projection(
     # has range [-1, 1] and the largest side has range [-u, u], with u > 1.
     # This convention is consistent with the PyTorch3D renderer, as well as
     # the transformation function `get_ndc_to_screen_transform`.
-    scale = (image_size_wh.to(R).min(dim=1, keepdim=True)[0] - 1) / 2.0
+    scale = image_size_wh.to(R).min(dim=1, keepdim=True)[0] / 2.0
     scale = scale.expand(-1, 2)
-    c0 = (image_size_wh - 1) / 2.0
+    c0 = image_size_wh / 2.0
 
     # Get the PyTorch3D focal length and principal point.
     focal_pytorch3d = focal_length / scale
@@ -75,9 +75,9 @@ def _opencv_from_cameras_projection(
     image_size_wh = image_size.to(R).flip(dims=(1,))
 
     # NDC to screen conversion.
-    scale = (image_size_wh.to(R).min(dim=1, keepdim=True)[0] - 1) / 2.0
+    scale = image_size_wh.to(R).min(dim=1, keepdim=True)[0] / 2.0
     scale = scale.expand(-1, 2)
-    c0 = (image_size_wh - 1) / 2.0
+    c0 = image_size_wh / 2.0
 
     # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch.Tensor.__neg__)[[Named...
     principal_point = -p0_pytorch3d * scale + c0

pytorch3d/renderer/cameras.py

@@ -36,8 +36,9 @@ class CamerasBase(TensorProperties):
         and translation (T)
     - NDC coordinate system: This is the normalized coordinate system that confines
         in a volume the rendered part of the object or scene. Also known as view volume.
-        For square images, given the PyTorch3D convention, (+1, +1, znear) is the top left near corner,
-        and (-1, -1, zfar) is the bottom right far corner of the volume.
+        For square images, given the PyTorch3D convention, (+1, +1, znear)
+        is the top left near corner, and (-1, -1, zfar) is the bottom right far
+        corner of the volume.
         The transformation from view --> NDC happens after applying the camera
         projection matrix (P) if defined in NDC space.
         For non square images, we scale the points such that smallest side
@@ -1623,12 +1624,12 @@ def get_ndc_to_screen_transform(
     # For non square images, we scale the points such that smallest side
     # has range [-1, 1] and the largest side has range [-u, u], with u > 1.
     # This convention is consistent with the PyTorch3D renderer
-    scale = (image_size.min(dim=1).values - 1.0) / 2.0
+    scale = (image_size.min(dim=1).values - 0.0) / 2.0
 
     K[:, 0, 0] = scale
     K[:, 1, 1] = scale
-    K[:, 0, 3] = -1.0 * (width - 1.0) / 2.0
-    K[:, 1, 3] = -1.0 * (height - 1.0) / 2.0
+    K[:, 0, 3] = -1.0 * (width - 0.0) / 2.0
+    K[:, 1, 3] = -1.0 * (height - 0.0) / 2.0
     K[:, 2, 2] = 1.0
     K[:, 3, 3] = 1.0
 

tests/test_cameras.py

@@ -130,9 +130,9 @@ def ndc_to_screen_points_naive(points, imsize):
     """
     height, width = imsize.unbind(1)
     width = width.view(-1, 1)
-    half_width = (width - 1.0) / 2.0
+    half_width = width / 2.0
     height = height.view(-1, 1)
-    half_height = (height - 1.0) / 2.0
+    half_height = height / 2.0
 
     scale = (
         half_width * (height > width).float() + half_height * (height <= width).float()
@@ -524,7 +524,7 @@ def init_equiv_cameras_ndc_screen(cam_type: CamerasBase, batch_size: int):
             # (height, width)
             image_size = torch.randint(low=2, high=64, size=(batch_size, 2))
             # scale
-            scale = (image_size.min(dim=1, keepdim=True).values - 1.0) / 2.0
+            scale = (image_size.min(dim=1, keepdim=True).values) / 2.0
 
             ndc_cam_params["focal_length"] = fcl
             ndc_cam_params["principal_point"] = prc
@@ -533,7 +533,7 @@ def init_equiv_cameras_ndc_screen(cam_type: CamerasBase, batch_size: int):
             screen_cam_params["image_size"] = image_size
             screen_cam_params["focal_length"] = fcl * scale
             screen_cam_params["principal_point"] = (
-                image_size[:, [1, 0]] - 1.0
+                image_size[:, [1, 0]]
             ) / 2.0 - prc * scale
             screen_cam_params["in_ndc"] = False
         else:
@@ -821,7 +821,7 @@ def test_transform_points(self):
     def test_perspective_type(self):
         cam = FoVPerspectiveCameras(znear=1.0, zfar=10.0, fov=60.0)
         self.assertTrue(cam.is_perspective())
-        self.assertEquals(cam.get_znear(), 1.0)
+        self.assertEqual(cam.get_znear(), 1.0)
 
 
 ############################################################
@@ -917,7 +917,7 @@ def test_orthographic_mixed_inputs_grad(self):
     def test_perspective_type(self):
         cam = FoVOrthographicCameras(znear=1.0, zfar=10.0)
         self.assertFalse(cam.is_perspective())
-        self.assertEquals(cam.get_znear(), 1.0)
+        self.assertEqual(cam.get_znear(), 1.0)
 
 
 ############################################################
@@ -974,7 +974,7 @@ def test_orthographic_kwargs(self):
     def test_perspective_type(self):
         cam = OrthographicCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
         self.assertFalse(cam.is_perspective())
-        self.assertEquals(cam.get_znear(), None)
+        self.assertIsNone(cam.get_znear())
 
 
 ############################################################
@@ -1026,4 +1026,4 @@ def test_perspective_kwargs(self):
     def test_perspective_type(self):
         cam = PerspectiveCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
         self.assertTrue(cam.is_perspective())
-        self.assertEquals(cam.get_znear(), None)
+        self.assertIsNone(cam.get_znear())

tests/test_render_meshes.py

@@ -250,23 +250,14 @@ def test_simple_sphere_screen(self):
         raster_settings = RasterizationSettings(
             image_size=512, blur_radius=0.0, faces_per_pixel=1
         )
+        half_half = (512.0 / 2.0, 512.0 / 2.0)
         for cam_type in (PerspectiveCameras, OrthographicCameras):
             cameras = cam_type(
                 device=device,
                 R=R,
                 T=T,
-                principal_point=(
-                    (
-                        (512.0 - 1.0) / 2.0,
-                        (512.0 - 1.0) / 2.0,
-                    ),
-                ),
-                focal_length=(
-                    (
-                        (512.0 - 1.0) / 2.0,
-                        (512.0 - 1.0) / 2.0,
-                    ),
-                ),
+                principal_point=(half_half,),
+                focal_length=(half_half,),
                 image_size=((512, 512),),
                 in_ndc=False,
             )
@@ -285,6 +276,10 @@ def test_simple_sphere_screen(self):
             images = renderer(sphere_mesh)
             rgb = images[0, ..., :3].squeeze().cpu()
             filename = "test_simple_sphere_light_phong_%s.png" % cam_type.__name__
+            if DEBUG:
+                Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / f"{filename}_.png"
+                )
 
             image_ref = load_rgb_image(filename, DATA_DIR)
             self.assertClose(rgb, image_ref, atol=0.05)