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

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