Update NDC raysampler for non-square convention (#29)

Summary:
- Old NDC convention had xy coords in [-1,1]x[-1,1]
- New NDC convention has xy coords in [-1, 1]x[-u, u] or [-u, u]x[-1, 1]

where u > 1 is the aspect ratio of the image.

This PR fixes the NDC raysampler to use the new convention.

Partial fix for #868

Pull Request resolved: fairinternal/pytorch3d#29

Reviewed By: davnov134

Differential Revision: D31926148

Pulled By: bottler

fbshipit-source-id: c6c42c60d1473b04e60ceb49c8c10951ddf03c74

pytorch3d/renderer/implicit/raysampling.py

@@ -139,8 +139,8 @@ class NDCGridRaysampler(GridRaysampler):
     have uniformly-spaced z-coordinates between a predefined minimum and maximum depth.
 
     `NDCGridRaysampler` follows the screen conventions of the `Meshes` and `Pointclouds`
-    renderers. I.e. the border of the leftmost / rightmost / topmost / bottommost pixel
-    has coordinates 1.0 / -1.0 / 1.0 / -1.0 respectively.
+    renderers. I.e. the pixel coordinates are in [-1, 1]x[-u, u] or [-u, u]x[-1, 1]
+    where u > 1 is the aspect ratio of the image.
     """
 
     def __init__(
@@ -159,13 +159,20 @@ def __init__(
             min_depth: The minimum depth of a ray-point.
             max_depth: The maximum depth of a ray-point.
         """
-        half_pix_width = 1.0 / image_width
-        half_pix_height = 1.0 / image_height
+        if image_width >= image_height:
+            range_x = image_width / image_height
+            range_y = 1.0
+        else:
+            range_x = 1.0
+            range_y = image_height / image_width
+
+        half_pix_width = range_x / image_width
+        half_pix_height = range_y / image_height
         super().__init__(
-            min_x=1.0 - half_pix_width,
-            max_x=-1.0 + half_pix_width,
-            min_y=1.0 - half_pix_height,
-            max_y=-1.0 + half_pix_height,
+            min_x=range_x - half_pix_width,
+            max_x=-range_x + half_pix_width,
+            min_y=range_y - half_pix_height,
+            max_y=-range_y + half_pix_height,
             image_width=image_width,
             image_height=image_height,
             n_pts_per_ray=n_pts_per_ray,

tests/test_raysampling.py

@@ -24,6 +24,69 @@
 from test_cameras import init_random_cameras
 
 
+class TestNDCRaysamplerConvention(TestCaseMixin, unittest.TestCase):
+    def setUp(self) -> None:
+        torch.manual_seed(42)
+
+    def test_ndc_convention(
+        self,
+        h=428,
+        w=760,
+    ):
+        device = torch.device("cuda")
+
+        camera = init_random_cameras(PerspectiveCameras, 1, random_z=True).to(device)
+
+        depth_map = torch.ones((1, 1, h, w)).to(device)
+
+        xyz = ray_bundle_to_ray_points(
+            NDCGridRaysampler(
+                image_width=w,
+                image_height=h,
+                n_pts_per_ray=1,
+                min_depth=1.0,
+                max_depth=1.0,
+            )(camera)._replace(lengths=depth_map[:, 0, ..., None])
+        ).view(1, -1, 3)
+
+        # project pointcloud
+        xy = camera.transform_points(xyz)[:, :, :2].squeeze()
+
+        xy_grid = self._get_ndc_grid(h, w, device)
+
+        self.assertClose(
+            xy,
+            xy_grid,
+            atol=1e-4,
+        )
+
+    def _get_ndc_grid(self, h, w, device):
+        if w >= h:
+            range_x = w / h
+            range_y = 1.0
+        else:
+            range_x = 1.0
+            range_y = h / w
+
+        half_pix_width = range_x / w
+        half_pix_height = range_y / h
+
+        min_x = range_x - half_pix_width
+        max_x = -range_x + half_pix_width
+        min_y = range_y - half_pix_height
+        max_y = -range_y + half_pix_height
+
+        y_grid, x_grid = torch.meshgrid(
+            torch.linspace(min_y, max_y, h, dtype=torch.float32),
+            torch.linspace(min_x, max_x, w, dtype=torch.float32),
+        )
+
+        x_points = x_grid.contiguous().view(-1).to(device)
+        y_points = y_grid.contiguous().view(-1).to(device)
+        xy = torch.stack((x_points, y_points), dim=1)
+        return xy
+
+
 class TestRaysampling(TestCaseMixin, unittest.TestCase):
     def setUp(self) -> None:
         torch.manual_seed(42)
@@ -147,12 +210,19 @@ def test_raysamplers(
 
                 if issubclass(raysampler_type, NDCGridRaysampler):
                     # adjust the gt bounds for NDCGridRaysampler
-                    half_pix_width = 1.0 / image_width
-                    half_pix_height = 1.0 / image_height
-                    min_x_ = 1.0 - half_pix_width
-                    max_x_ = -1.0 + half_pix_width
-                    min_y_ = 1.0 - half_pix_height
-                    max_y_ = -1.0 + half_pix_height
+                    if image_width >= image_height:
+                        range_x = image_width / image_height
+                        range_y = 1.0
+                    else:
+                        range_x = 1.0
+                        range_y = image_height / image_width
+
+                    half_pix_width = range_x / image_width
+                    half_pix_height = range_y / image_height
+                    min_x_ = range_x - half_pix_width
+                    max_x_ = -range_x + half_pix_width
+                    min_y_ = range_y - half_pix_height
+                    max_y_ = -range_y + half_pix_height
                 else:
                     min_x_ = min_x
                     max_x_ = max_x

tests/test_render_implicit.py

@@ -159,8 +159,12 @@ def test_input_types(self):
             with self.assertRaises(ValueError):
                 renderer(cameras=cameras, volumetric_function=bad_volumetric_function)
 
-    def test_compare_with_meshes_renderer(
-        self, batch_size=11, image_size=100, sphere_diameter=0.6
+    def test_compare_with_meshes_renderer(self):
+        self._compare_with_meshes_renderer(image_size=(200, 100))
+        self._compare_with_meshes_renderer(image_size=(100, 200))
+
+    def _compare_with_meshes_renderer(
+        self, image_size, batch_size=11, sphere_diameter=0.6
     ):
         """
         Generate a spherical RGB volumetric function and its corresponding mesh
@@ -169,18 +173,16 @@ def test_compare_with_meshes_renderer(
         """
 
         # generate NDC camera extrinsics and intrinsics
-        cameras = init_cameras(
-            batch_size, image_size=[image_size, image_size], ndc=True
-        )
+        cameras = init_cameras(batch_size, image_size=image_size, ndc=True)
 
         # get rand offset of the volume
         sphere_centroid = torch.randn(batch_size, 3, device=cameras.device) * 0.1
         sphere_centroid.requires_grad = True
 
         # init the grid raysampler with the ndc grid
         raysampler = NDCGridRaysampler(
-            image_width=image_size,
-            image_height=image_size,
+            image_width=image_size[1],
+            image_height=image_size[0],
             n_pts_per_ray=256,
             min_depth=0.1,
             max_depth=2.0,
@@ -336,9 +338,11 @@ def test_compare_with_meshes_renderer(
         self.assertClose(mu_diff, torch.zeros_like(mu_diff), atol=5e-2)
         self.assertClose(std_diff, torch.zeros_like(std_diff), atol=6e-2)
 
-    def test_rotating_gif(
-        self, n_frames=50, fps=15, image_size=(100, 100), sphere_diameter=0.5
-    ):
+    def test_rotating_gif(self):
+        self._rotating_gif(image_size=(200, 100))
+        self._rotating_gif(image_size=(100, 200))
+
+    def _rotating_gif(self, image_size, n_frames=50, fps=15, sphere_diameter=0.5):
         """
         Render a gif animation of a rotating sphere (runs only if `DEBUG==True`).
         """

tests/test_render_volumes.py

@@ -164,7 +164,7 @@ def init_cameras(
         p0 = torch.ones(batch_size, 2, device=device)
         p0[:, 0] *= image_size[1] * 0.5
         p0[:, 1] *= image_size[0] * 0.5
-        focal = image_size[0] * torch.ones(batch_size, device=device)
+        focal = max(*image_size) * torch.ones(batch_size, device=device)
 
     # convert to a Camera object
     cameras = PerspectiveCameras(focal, p0, R=R, T=T, device=device)
@@ -295,17 +295,15 @@ def test_input_types(self, batch_size: int = 10):
                 _validate_ray_bundle_variables(*bad_ray_bundle)
 
     def test_compare_with_pointclouds_renderer(
-        self, batch_size=11, volume_size=(30, 30, 30), image_size=200
+        self, batch_size=11, volume_size=(30, 30, 30), image_size=(200, 250)
     ):
         """
         Generate a volume and its corresponding point cloud and check whether
         PointsRenderer returns the same images as the corresponding VolumeRenderer.
         """
 
         # generate NDC camera extrinsics and intrinsics
-        cameras = init_cameras(
-            batch_size, image_size=[image_size, image_size], ndc=True
-        )
+        cameras = init_cameras(batch_size, image_size=image_size, ndc=True)
 
         # init the boundary volume
         for shape in ("sphere", "cube"):
@@ -340,10 +338,10 @@ def test_compare_with_pointclouds_renderer(
 
             # init the grid raysampler with the ndc grid
             coord_range = 1.0
-            half_pix_size = coord_range / image_size
+            half_pix_size = coord_range / max(*image_size)
             raysampler = NDCGridRaysampler(
-                image_width=image_size,
-                image_height=image_size,
+                image_width=image_size[1],
+                image_height=image_size[0],
                 n_pts_per_ray=256,
                 min_depth=0.1,
                 max_depth=2.0,
@@ -499,8 +497,12 @@ def test_monte_carlo_rendering(
             images_opacities_mc.permute(0, 3, 1, 2), images_opacities_mc_, atol=1e-4
         )
 
-    def test_rotating_gif(
-        self, n_frames=50, fps=15, volume_size=(100, 100, 100), image_size=(100, 100)
+    def test_rotating_gif(self):
+        self._rotating_gif(image_size=(200, 100))
+        self._rotating_gif(image_size=(100, 200))
+
+    def _rotating_gif(
+        self, image_size, n_frames=50, fps=15, volume_size=(100, 100, 100)
     ):
         """
         Render a gif animation of a rotating cube/sphere (runs only if `DEBUG==True`).
@@ -586,7 +588,7 @@ def test_rotating_cube_volume_render(self):
 
         # batch_size = 4 sides of the cube
         batch_size = 4
-        image_size = (50, 50)
+        image_size = (50, 40)
 
         for volume_size in ([25, 25, 25],):
             for sample_mode in ("bilinear", "nearest"):