coarse rasterization bug fix

Summary:
Fix a bug which resulted in a rendering artifacts if the image size was not a multiple of 16.
Fix: Revert coarse rasterization to original implementation and only update fine rasterization to reverse the ordering of Y and X axis. This is much simpler than the previous approach!

Additional changes:
- updated mesh rendering end-end tests to check outputs from both naive and coarse to fine rasterization.
- added pointcloud rendering end-end tests

Reviewed By: gkioxari

Differential Revision: D21102725

fbshipit-source-id: 2e7e1b013dd6dd12b3a00b79eb8167deddb2e89a

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu

@@ -556,18 +556,16 @@ __global__ void RasterizeMeshesCoarseCudaKernel(
         // PixToNdc gives the location of the center of each pixel, so we
         // need to add/subtract a half pixel to get the true extent of the bin.
         // Reverse ordering of Y axis so that +Y is upwards in the image.
-        const int yidx = num_bins - by;
-        const float bin_y_max = PixToNdc(yidx * bin_size - 1, H) + half_pix;
-        const float bin_y_min = PixToNdc((yidx - 1) * bin_size, H) - half_pix;
-
+        const float bin_y_min = PixToNdc(by * bin_size, H) - half_pix;
+        const float bin_y_max = PixToNdc((by + 1) * bin_size - 1, H) + half_pix;
         const bool y_overlap = (ymin <= bin_y_max) && (bin_y_min < ymax);
 
         for (int bx = 0; bx < num_bins; ++bx) {
           // X coordinate of the left and right of the bin.
           // Reverse ordering of x axis so that +X is left.
-          const int xidx = num_bins - bx;
-          const float bin_x_max = PixToNdc(xidx * bin_size - 1, W) + half_pix;
-          const float bin_x_min = PixToNdc((xidx - 1) * bin_size, W) - half_pix;
+          const float bin_x_max =
+              PixToNdc((bx + 1) * bin_size - 1, W) + half_pix;
+          const float bin_x_min = PixToNdc(bx * bin_size, W) - half_pix;
 
           const bool x_overlap = (xmin <= bin_x_max) && (bin_x_min < xmax);
           if (y_overlap && x_overlap) {
@@ -629,6 +627,7 @@ torch::Tensor RasterizeMeshesCoarseCuda(
   const int N = num_faces_per_mesh.size(0);
   const int num_bins = 1 + (image_size - 1) / bin_size; // Divide round up.
   const int M = max_faces_per_bin;
+
   if (num_bins >= 22) {
     std::stringstream ss;
     ss << "Got " << num_bins << "; that's too many!";
@@ -702,13 +701,8 @@ __global__ void RasterizeMeshesFineCudaKernel(
     if (yi >= H || xi >= W)
       continue;
 
-    // Reverse ordering of the X and Y axis so that
-    // in the image +Y is pointing up and +X is pointing left.
-    const int yidx = H - 1 - yi;
-    const int xidx = W - 1 - xi;
-
-    const float xf = PixToNdc(xidx, W);
-    const float yf = PixToNdc(yidx, H);
+    const float xf = PixToNdc(xi, W);
+    const float yf = PixToNdc(yi, H);
     const float2 pxy = make_float2(xf, yf);
 
     // This part looks like the naive rasterization kernel, except we use
@@ -743,7 +737,12 @@ __global__ void RasterizeMeshesFineCudaKernel(
     // output for the current pixel.
     // TODO: make sorting an option as only top k is needed, not sorted values.
     BubbleSort(q, q_size);
-    const int pix_idx = n * H * W * K + yi * H * K + xi * K;
+
+    // Reverse ordering of the X and Y axis so that
+    // in the image +Y is pointing up and +X is pointing left.
+    const int yidx = H - 1 - yi;
+    const int xidx = W - 1 - xi;
+    const int pix_idx = n * H * W * K + yidx * H * K + xidx * K;
     for (int k = 0; k < q_size; k++) {
       face_idxs[pix_idx + k] = q[k].idx;
       zbuf[pix_idx + k] = q[k].z;

pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp

@@ -430,13 +430,13 @@ torch::Tensor RasterizeMeshesCoarseCpu(
     const int face_stop_idx =
         (face_start_idx + num_faces_per_mesh[n].item().to<int32_t>());
 
-    float bin_y_max = 1.0f;
-    float bin_y_min = bin_y_max - bin_width;
+    float bin_y_min = -1.0f;
+    float bin_y_max = bin_y_min + bin_width;
 
     // Iterate through the horizontal bins from top to bottom.
     for (int by = 0; by < BH; ++by) {
-      float bin_x_max = 1.0f;
-      float bin_x_min = bin_x_max - bin_width;
+      float bin_x_min = -1.0f;
+      float bin_x_max = bin_x_min + bin_width;
 
       // Iterate through bins on this horizontal line, left to right.
       for (int bx = 0; bx < BW; ++bx) {
@@ -473,13 +473,13 @@ torch::Tensor RasterizeMeshesCoarseCpu(
           }
         }
 
-        // Shift the bin to the left for the next loop iteration.
-        bin_x_max = bin_x_min;
-        bin_x_min = bin_x_min - bin_width;
+        // Shift the bin to the right for the next loop iteration
+        bin_x_min = bin_x_max;
+        bin_x_max = bin_x_min + bin_width;
       }
-      // Shift the bin down for the next loop iteration.
-      bin_y_max = bin_y_min;
-      bin_y_min = bin_y_min - bin_width;
+      // Shift the bin down for the next loop iteration
+      bin_y_min = bin_y_max;
+      bin_y_max = bin_y_min + bin_width;
     }
   }
   return bin_faces;

pytorch3d/csrc/rasterize_points/rasterize_points.cu

@@ -95,7 +95,8 @@ __global__ void RasterizePointsNaiveCudaKernel(
     const int n = i / (S * S); // Batch index
     const int pix_idx = i % (S * S);
 
-    // Reverse ordering of X and Y axes.
+    // Reverse ordering of the X and Y axis as the camera coordinates
+    // assume that +Y is pointing up and +X is pointing left.
     const int yi = S - 1 - pix_idx / S;
     const int xi = S - 1 - pix_idx % S;
 
@@ -260,23 +261,20 @@ __global__ void RasterizePointsCoarseCudaKernel(
         // Get y extent for the bin. PixToNdc gives us the location of
         // the center of each pixel, so we need to add/subtract a half
         // pixel to get the true extent of the bin.
-        // Reverse ordering of Y axis so that +Y is upwards in the image.
-        const int yidx = num_bins - by;
-        const float bin_y_max = PixToNdc(yidx * bin_size - 1, S) + half_pix;
-        const float bin_y_min = PixToNdc((yidx - 1) * bin_size, S) - half_pix;
+        const float by0 = PixToNdc(by * bin_size, S) - half_pix;
+        const float by1 = PixToNdc((by + 1) * bin_size - 1, S) + half_pix;
+        const bool y_overlap = (py0 <= by1) && (by0 <= py1);
 
-        const bool y_overlap = (py0 <= bin_y_max) && (bin_y_min <= py1);
         if (!y_overlap) {
           continue;
         }
         for (int bx = 0; bx < num_bins; ++bx) {
           // Get x extent for the bin; again we need to adjust the
           // output of PixToNdc by half a pixel.
-          // Reverse ordering of x axis so that +X is left.
-          const int xidx = num_bins - bx;
-          const float bin_x_max = PixToNdc(xidx * bin_size - 1, S) + half_pix;
-          const float bin_x_min = PixToNdc((xidx - 1) * bin_size, S) - half_pix;
-          const bool x_overlap = (px0 <= bin_x_max) && (bin_x_min <= px1);
+          const float bx0 = PixToNdc(bx * bin_size, S) - half_pix;
+          const float bx1 = PixToNdc((bx + 1) * bin_size - 1, S) + half_pix;
+          const bool x_overlap = (px0 <= bx1) && (bx0 <= px1);
+
           if (x_overlap) {
             binmask.set(by, bx, p);
           }
@@ -330,6 +328,7 @@ torch::Tensor RasterizePointsCoarseCuda(
   const int N = num_points_per_cloud.size(0);
   const int num_bins = 1 + (image_size - 1) / bin_size; // divide round up
   const int M = max_points_per_bin;
+
   if (points.ndimension() != 2 || points.size(1) != 3) {
     AT_ERROR("points must have dimensions (num_points, 3)");
   }
@@ -346,6 +345,7 @@ torch::Tensor RasterizePointsCoarseCuda(
   const size_t shared_size = num_bins * num_bins * chunk_size / 8;
   const size_t blocks = 64;
   const size_t threads = 512;
+
   RasterizePointsCoarseCudaKernel<<<blocks, threads, shared_size>>>(
       points.contiguous().data_ptr<float>(),
       cloud_to_packed_first_idx.contiguous().data_ptr<int64_t>(),
@@ -372,7 +372,7 @@ __global__ void RasterizePointsFineCudaKernel(
     const float radius,
     const int bin_size,
     const int N,
-    const int B,
+    const int B, // num_bins
     const int M,
     const int S,
     const int K,
@@ -397,19 +397,15 @@ __global__ void RasterizePointsFineCudaKernel(
     i %= B * bin_size * bin_size;
     const int bx = i / (bin_size * bin_size);
     i %= bin_size * bin_size;
+
     const int yi = i / bin_size + by * bin_size;
     const int xi = i % bin_size + bx * bin_size;
 
     if (yi >= S || xi >= S)
       continue;
 
-    // Reverse ordering of the X and Y axis so that
-    // in the image +Y is pointing up and +X is pointing left.
-    const int yidx = S - 1 - yi;
-    const int xidx = S - 1 - xi;
-
-    const float xf = PixToNdc(xidx, S);
-    const float yf = PixToNdc(yidx, S);
+    const float xf = PixToNdc(xi, S);
+    const float yf = PixToNdc(yi, S);
 
     // This part looks like the naive rasterization kernel, except we use
     // bin_points to only look at a subset of points already known to fall
@@ -431,7 +427,13 @@ __global__ void RasterizePointsFineCudaKernel(
     // Now we've looked at all the points for this bin, so we can write
     // output for the current pixel.
     BubbleSort(q, q_size);
-    const int pix_idx = n * S * S * K + yi * S * K + xi * K;
+
+    // Reverse ordering of the X and Y axis as the camera coordinates
+    // assume that +Y is pointing up and +X is pointing left.
+    const int yidx = S - 1 - yi;
+    const int xidx = S - 1 - xi;
+
+    const int pix_idx = n * S * S * K + yidx * S * K + xidx * K;
     for (int k = 0; k < q_size; ++k) {
       point_idxs[pix_idx + k] = q[k].idx;
       zbuf[pix_idx + k] = q[k].z;
@@ -448,7 +450,7 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsFineCuda(
     const int bin_size,
     const int points_per_pixel) {
   const int N = bin_points.size(0);
-  const int B = bin_points.size(1);
+  const int B = bin_points.size(1); // num_bins
   const int M = bin_points.size(3);
   const int S = image_size;
   const int K = points_per_pixel;

pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp

@@ -125,13 +125,13 @@ torch::Tensor RasterizePointsCoarseCpu(
     const int point_stop_idx =
         (point_start_idx + num_points_per_cloud[n].item().to<int32_t>());
 
-    float bin_y_max = 1.0f;
-    float bin_y_min = bin_y_max - bin_width;
+    float bin_y_min = -1.0f;
+    float bin_y_max = bin_y_min + bin_width;
 
     // Iterate through the horizontal bins from top to bottom.
     for (int by = 0; by < B; by++) {
-      float bin_x_max = 1.0f;
-      float bin_x_min = bin_x_max - bin_width;
+      float bin_x_min = -1.0f;
+      float bin_x_max = bin_x_min + bin_width;
 
       // Iterate through bins on this horizontal line, left to right.
       for (int bx = 0; bx < B; bx++) {
@@ -166,13 +166,13 @@ torch::Tensor RasterizePointsCoarseCpu(
         // Record the number of points found in this bin
         points_per_bin_a[n][by][bx] = points_hit;
 
-        // Shift the bin to the left for the next loop iteration.
-        bin_x_max = bin_x_min;
-        bin_x_min = bin_x_min - bin_width;
+        // Shift the bin to the right for the next loop iteration
+        bin_x_min = bin_x_max;
+        bin_x_max = bin_x_min + bin_width;
       }
-      // Shift the bin down for the next loop iteration.
-      bin_y_max = bin_y_min;
-      bin_y_min = bin_y_min - bin_width;
+      // Shift the bin down for the next loop iteration
+      bin_y_min = bin_y_max;
+      bin_y_max = bin_y_min + bin_width;
     }
   }
   return bin_points;

pytorch3d/renderer/mesh/rasterize_meshes.py

@@ -9,7 +9,7 @@ from pytorch3d import _C
 
 
 # TODO make the epsilon user configurable
-kEpsilon = 1e-30
+kEpsilon = 1e-8
 
 
 def rasterize_meshes(

pytorch3d/renderer/points/rasterizer.py

@@ -19,12 +19,28 @@ class PointFragments(NamedTuple):
 
 
 # Class to store the point rasterization params with defaults
-class PointsRasterizationSettings(NamedTuple):
-    image_size: int = 256
-    radius: float = 0.01
-    points_per_pixel: int = 8
-    bin_size: Optional[int] = None
-    max_points_per_bin: Optional[int] = None
+class PointsRasterizationSettings:
+    __slots__ = [
+        "image_size",
+        "radius",
+        "points_per_pixel",
+        "bin_size",
+        "max_points_per_bin",
+    ]
+
+    def __init__(
+        self,
+        image_size: int = 256,
+        radius: float = 0.01,
+        points_per_pixel: int = 8,
+        bin_size: Optional[int] = None,
+        max_points_per_bin: Optional[int] = None,
+    ):
+        self.image_size = image_size
+        self.radius = radius
+        self.points_per_pixel = points_per_pixel
+        self.bin_size = bin_size
+        self.max_points_per_bin = max_points_per_bin
 
 
 class PointsRasterizer(nn.Module):

tests/common_testing.py

@@ -1,10 +1,20 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
+from pathlib import Path
 from typing import Callable, Optional, Union
 
 import numpy as np
 import torch
+from PIL import Image
+
+
+def load_rgb_image(filename: str, data_dir: Union[str, Path]):
+    filepath = data_dir / filename
+    with Image.open(filepath) as raw_image:
+        image = torch.from_numpy(np.array(raw_image) / 255.0)
+    image = image.to(dtype=torch.float32)
+    return image[..., :3]
 
 
 TensorOrArray = Union[torch.Tensor, np.ndarray]

tests/test_rasterize_meshes.py

@@ -896,10 +896,10 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
             torch.ones((1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device)
             * -1
         )
-        bin_faces_expected[0, 0, 0, 0] = torch.tensor([1])
-        bin_faces_expected[0, 1, 0, 0:2] = torch.tensor([1, 2])
-        bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([0, 1])
         bin_faces_expected[0, 1, 1, 0] = torch.tensor([1])
+        bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([1, 2])
+        bin_faces_expected[0, 1, 0, 0:2] = torch.tensor([0, 1])
+        bin_faces_expected[0, 0, 0, 0] = torch.tensor([1])
 
         # +Y up, +X left, +Z in
         bin_faces = _C._rasterize_meshes_coarse(
@@ -911,7 +911,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
             bin_size,
             max_faces_per_bin,
         )
-        # Flip x and y axis of output before comparing to expected
+
         bin_faces_same = (bin_faces.squeeze() == bin_faces_expected).all()
         self.assertTrue(bin_faces_same.item() == 1)
 

tests/test_rasterize_points.py

@@ -434,23 +434,21 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
 
     def _test_coarse_rasterize(self, device):
         #
-        #  Note that +Y is up and +X is left in the diagram below.
         #
-        #  (4)              |2
-        #                   |
-        #                   |
-        #                   |
-        #                   |1
-        #                   |
-        #             (1)   |
-        #                   | (2)
-        # ____________(0)__(5)___________________
-        # 2        1        |          -1      -2
-        #                   |
-        #       (3)         |
-        #                   |
-        #                   |-1
-        #                   |
+        #           |2                  (4)
+        #           |
+        #           |
+        #           |
+        #           |1
+        #           |
+        #           |    (1)
+        #        (2)|
+        # _________(5)___(0)_______________
+        # -1        |           1         2
+        #           |
+        #           |            (3)
+        #           |
+        #           |-1
         #
         # Locations of the points are shown by o. The screen bounding box
         # is between [-1, 1] in both the x and y directions.
@@ -486,9 +484,9 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
         # fit in one chunk. This will the the case for this small example, but
         # to properly exercise coordianted writes among multiple chunks we need
         # to use a bigger test case.
-        bin_points_expected[0, 1, 0, :2] = torch.tensor([0, 3])
-        bin_points_expected[0, 0, 1, 0] = torch.tensor([2])
-        bin_points_expected[0, 0, 0, :2] = torch.tensor([0, 1])
+        bin_points_expected[0, 0, 1, :2] = torch.tensor([0, 3])
+        bin_points_expected[0, 1, 0, 0] = torch.tensor([2])
+        bin_points_expected[0, 1, 1, :2] = torch.tensor([0, 1])
 
         pointclouds = Pointclouds(points=[points])
         args = (
@@ -502,4 +500,5 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
         )
         bin_points = _C._rasterize_points_coarse(*args)
         bin_points_same = (bin_points == bin_points_expected).all()
+
         self.assertTrue(bin_points_same.item() == 1)

tests/test_render_meshes.py

@@ -9,6 +9,7 @@ from pathlib import Path
 
 import numpy as np
 import torch
+from common_testing import TestCaseMixin, load_rgb_image
 from PIL import Image
 from pytorch3d.io import load_objs_as_meshes
 from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform
@@ -35,15 +36,7 @@ DEBUG = False
 DATA_DIR = Path(__file__).resolve().parent / "data"
 
 
-def load_rgb_image(filename, data_dir=DATA_DIR):
-    filepath = data_dir / filename
-    with Image.open(filepath) as raw_image:
-        image = torch.from_numpy(np.array(raw_image) / 255.0)
-    image = image.to(dtype=torch.float32)
-    return image[..., :3]
-
-
-class TestRenderingMeshes(unittest.TestCase):
+class TestRenderMeshes(TestCaseMixin, unittest.TestCase):
     def test_simple_sphere(self, elevated_camera=False):
         """
         Test output of phong and gouraud shading matches a reference image using
@@ -81,7 +74,7 @@ class TestRenderingMeshes(unittest.TestCase):
         lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
 
         raster_settings = RasterizationSettings(
-            image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
+            image_size=512, blur_radius=0.0, faces_per_pixel=1
         )
         rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
 
@@ -96,14 +89,14 @@ class TestRenderingMeshes(unittest.TestCase):
             renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
             images = renderer(sphere_mesh)
             filename = "simple_sphere_light_%s%s.png" % (name, postfix)
-            image_ref = load_rgb_image("test_%s" % filename)
+            image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
             rgb = images[0, ..., :3].squeeze().cpu()
             if DEBUG:
-                filename = "DEBUG_" % filename
+                filename = "DEBUG_%s" % filename
                 Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
                     DATA_DIR / filename
                 )
-            self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05))
+            self.assertClose(rgb, image_ref, atol=0.05)
 
         ########################################################
         # Move the light to the +z axis in world space so it is
@@ -124,8 +117,10 @@ class TestRenderingMeshes(unittest.TestCase):
             )
 
         # Load reference image
-        image_ref_phong_dark = load_rgb_image("test_simple_sphere_dark%s.png" % postfix)
-        self.assertTrue(torch.allclose(rgb, image_ref_phong_dark, atol=0.05))
+        image_ref_phong_dark = load_rgb_image(
+            "test_simple_sphere_dark%s.png" % postfix, DATA_DIR
+        )
+        self.assertClose(rgb, image_ref_phong_dark, atol=0.05)
 
     def test_simple_sphere_elevated_camera(self):
         """
@@ -160,7 +155,7 @@ class TestRenderingMeshes(unittest.TestCase):
         R, T = look_at_view_transform(dist, elev, azim)
         cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
         raster_settings = RasterizationSettings(
-            image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
+            image_size=512, blur_radius=0.0, faces_per_pixel=1
         )
 
         # Init shader settings
@@ -179,10 +174,12 @@ class TestRenderingMeshes(unittest.TestCase):
             shader = shader_init(lights=lights, cameras=cameras, materials=materials)
             renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
             images = renderer(sphere_meshes)
-            image_ref = load_rgb_image("test_simple_sphere_light_%s.png" % name)
+            image_ref = load_rgb_image(
+                "test_simple_sphere_light_%s.png" % name, DATA_DIR
+            )
             for i in range(batch_size):
                 rgb = images[i, ..., :3].squeeze().cpu()
-                self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05))
+                self.assertClose(rgb, image_ref, atol=0.05)
 
     def test_silhouette_with_grad(self):
         """
@@ -200,7 +197,6 @@ class TestRenderingMeshes(unittest.TestCase):
             image_size=512,
             blur_radius=np.log(1.0 / 1e-4 - 1.0) * blend_params.sigma,
             faces_per_pixel=80,
-            bin_size=0,
         )
 
         # Init rasterizer settings
@@ -222,7 +218,7 @@ class TestRenderingMeshes(unittest.TestCase):
         with Image.open(image_ref_filename) as raw_image_ref:
             image_ref = torch.from_numpy(np.array(raw_image_ref))
         image_ref = image_ref.to(dtype=torch.float32) / 255.0
-        self.assertTrue(torch.allclose(alpha, image_ref, atol=0.055))
+        self.assertClose(alpha, image_ref, atol=0.055)
 
         # Check grad exist
         verts.requires_grad = True
@@ -237,8 +233,8 @@ class TestRenderingMeshes(unittest.TestCase):
         The pupils in the eyes of the cow should always be looking to the left.
         """
         device = torch.device("cuda:0")
-        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-        obj_filename = DATA_DIR / "cow_mesh/cow.obj"
+        obj_dir = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
+        obj_filename = obj_dir / "cow_mesh/cow.obj"
 
         # Load mesh + texture
         mesh = load_objs_as_meshes([obj_filename], device=device)
@@ -247,7 +243,7 @@ class TestRenderingMeshes(unittest.TestCase):
         R, T = look_at_view_transform(2.7, 0, 0)
         cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
         raster_settings = RasterizationSettings(
-            image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
+            image_size=512, blur_radius=0.0, faces_per_pixel=1
         )
 
         # Init shader settings
@@ -265,22 +261,26 @@ class TestRenderingMeshes(unittest.TestCase):
                 lights=lights, cameras=cameras, materials=materials
             ),
         )
-        images = renderer(mesh)
-        rgb = images[0, ..., :3].squeeze().cpu()
 
         # Load reference image
-        image_ref = load_rgb_image("test_texture_map_back.png")
+        image_ref = load_rgb_image("test_texture_map_back.png", DATA_DIR)
 
-        if DEBUG:
-            Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
-                DATA_DIR / "DEBUG_texture_map_back.png"
-            )
+        for bin_size in [0, None]:
+            # Check both naive and coarse to fine produce the same output.
+            renderer.rasterizer.raster_settings.bin_size = bin_size
+            images = renderer(mesh)
+            rgb = images[0, ..., :3].squeeze().cpu()
 
-        # NOTE some pixels can be flaky and will not lead to
-        # `cond1` being true. Add `cond2` and check `cond1 or cond2`
-        cond1 = torch.allclose(rgb, image_ref, atol=0.05)
-        cond2 = ((rgb - image_ref).abs() > 0.05).sum() < 5
-        self.assertTrue(cond1 or cond2)
+            if DEBUG:
+                Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / "DEBUG_texture_map_back.png"
+                )
+
+            # NOTE some pixels can be flaky and will not lead to
+            # `cond1` being true. Add `cond2` and check `cond1 or cond2`
+            cond1 = torch.allclose(rgb, image_ref, atol=0.05)
+            cond2 = ((rgb - image_ref).abs() > 0.05).sum() < 5
+            self.assertTrue(cond1 or cond2)
 
         # Check grad exists
         [verts] = mesh.verts_list()
@@ -299,16 +299,27 @@ class TestRenderingMeshes(unittest.TestCase):
 
         # Move light to the front of the cow in world space
         lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
-        images = renderer(mesh, cameras=cameras, lights=lights)
-        rgb = images[0, ..., :3].squeeze().cpu()
 
         # Load reference image
-        image_ref = load_rgb_image("test_texture_map_front.png")
+        image_ref = load_rgb_image("test_texture_map_front.png", DATA_DIR)
 
-        if DEBUG:
-            Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
-                DATA_DIR / "DEBUG_texture_map_front.png"
-            )
+        for bin_size in [0, None]:
+            # Check both naive and coarse to fine produce the same output.
+            renderer.rasterizer.raster_settings.bin_size = bin_size
+
+            images = renderer(mesh, cameras=cameras, lights=lights)
+            rgb = images[0, ..., :3].squeeze().cpu()
+
+            if DEBUG:
+                Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / "DEBUG_texture_map_front.png"
+                )
+
+            # NOTE some pixels can be flaky and will not lead to
+            # `cond1` being true. Add `cond2` and check `cond1 or cond2`
+            cond1 = torch.allclose(rgb, image_ref, atol=0.05)
+            cond2 = ((rgb - image_ref).abs() > 0.05).sum() < 5
+            self.assertTrue(cond1 or cond2)
 
         #################################
         # Add blurring to rasterization
@@ -320,23 +331,26 @@ class TestRenderingMeshes(unittest.TestCase):
             image_size=512,
             blur_radius=np.log(1.0 / 1e-4 - 1.0) * blend_params.sigma,
             faces_per_pixel=100,
-            bin_size=0,
         )
 
-        images = renderer(
-            mesh.clone(),
-            cameras=cameras,
-            raster_settings=raster_settings,
-            blend_params=blend_params,
-        )
-        rgb = images[0, ..., :3].squeeze().cpu()
-
         # Load reference image
-        image_ref = load_rgb_image("test_blurry_textured_rendering.png")
+        image_ref = load_rgb_image("test_blurry_textured_rendering.png", DATA_DIR)
 
-        if DEBUG:
-            Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
-                DATA_DIR / "DEBUG_blurry_textured_rendering.png"
+        for bin_size in [0, None]:
+            # Check both naive and coarse to fine produce the same output.
+            renderer.rasterizer.raster_settings.bin_size = bin_size
+
+            images = renderer(
+                mesh.clone(),
+                cameras=cameras,
+                raster_settings=raster_settings,
+                blend_params=blend_params,
             )
+            rgb = images[0, ..., :3].squeeze().cpu()
 
-        self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05))
+            if DEBUG:
+                Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / "DEBUG_blurry_textured_rendering.png"
+                )
+
+            self.assertClose(rgb, image_ref, atol=0.05)

tests/test_render_points.py

@@ -0,0 +1,173 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+"""
+Sanity checks for output images from the pointcloud renderer.
+"""
+import unittest
+import warnings
+from os import path
+from pathlib import Path
+
+import numpy as np
+import torch
+from common_testing import TestCaseMixin, load_rgb_image
+from PIL import Image
+from pytorch3d.renderer.cameras import (
+    OpenGLOrthographicCameras,
+    OpenGLPerspectiveCameras,
+    look_at_view_transform,
+)
+from pytorch3d.renderer.points import (
+    AlphaCompositor,
+    NormWeightedCompositor,
+    PointsRasterizationSettings,
+    PointsRasterizer,
+    PointsRenderer,
+)
+from pytorch3d.structures.pointclouds import Pointclouds
+from pytorch3d.utils.ico_sphere import ico_sphere
+
+
+# If DEBUG=True, save out images generated in the tests for debugging.
+# All saved images have prefix DEBUG_
+DEBUG = False
+DATA_DIR = Path(__file__).resolve().parent / "data"
+
+
+class TestRenderPoints(TestCaseMixin, unittest.TestCase):
+    def test_simple_sphere(self):
+        device = torch.device("cuda:0")
+        sphere_mesh = ico_sphere(1, device)
+        verts_padded = sphere_mesh.verts_padded()
+        # Shift vertices to check coordinate frames are correct.
+        verts_padded[..., 1] += 0.2
+        verts_padded[..., 0] += 0.2
+        pointclouds = Pointclouds(
+            points=verts_padded, features=torch.ones_like(verts_padded)
+        )
+        R, T = look_at_view_transform(2.7, 0.0, 0.0)
+        cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
+        raster_settings = PointsRasterizationSettings(
+            image_size=256, radius=5e-2, points_per_pixel=1
+        )
+        rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
+        compositor = NormWeightedCompositor()
+        renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
+
+        # Load reference image
+        filename = "simple_pointcloud_sphere.png"
+        image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
+
+        for bin_size in [0, None]:
+            # Check both naive and coarse to fine produce the same output.
+            renderer.rasterizer.raster_settings.bin_size = bin_size
+            images = renderer(pointclouds)
+            rgb = images[0, ..., :3].squeeze().cpu()
+            if DEBUG:
+                filename = "DEBUG_%s" % filename
+                Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / filename
+                )
+            self.assertClose(rgb, image_ref)
+
+    def test_pointcloud_with_features(self):
+        device = torch.device("cuda:0")
+        file_dir = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
+        pointcloud_filename = file_dir / "PittsburghBridge/pointcloud.npz"
+
+        # Note, this file is too large to check in to the repo.
+        # Download the file to run the test locally.
+        if not path.exists(pointcloud_filename):
+            url = "https://dl.fbaipublicfiles.com/pytorch3d/data/PittsburghBridge/pointcloud.npz"
+            msg = (
+                "pointcloud.npz not found, download from %s, save it at the path %s, and rerun"
+                % (url, pointcloud_filename)
+            )
+            warnings.warn(msg)
+            return True
+
+        # Load point cloud
+        pointcloud = np.load(pointcloud_filename)
+        verts = torch.Tensor(pointcloud["verts"]).to(device)
+        rgb_feats = torch.Tensor(pointcloud["rgb"]).to(device)
+
+        verts.requires_grad = True
+        rgb_feats.requires_grad = True
+        point_cloud = Pointclouds(points=[verts], features=[rgb_feats])
+
+        R, T = look_at_view_transform(20, 10, 0)
+        cameras = OpenGLOrthographicCameras(device=device, R=R, T=T, znear=0.01)
+
+        raster_settings = PointsRasterizationSettings(
+            # Set image_size so it is not a multiple of 16 (min bin_size)
+            # in order to confirm that there are no errors in coarse rasterization.
+            image_size=500,
+            radius=0.003,
+            points_per_pixel=10,
+        )
+
+        renderer = PointsRenderer(
+            rasterizer=PointsRasterizer(
+                cameras=cameras, raster_settings=raster_settings
+            ),
+            compositor=AlphaCompositor(),
+        )
+
+        images = renderer(point_cloud)
+
+        # Load reference image
+        filename = "bridge_pointcloud.png"
+        image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
+
+        for bin_size in [0, None]:
+            # Check both naive and coarse to fine produce the same output.
+            renderer.rasterizer.raster_settings.bin_size = bin_size
+            images = renderer(point_cloud)
+            rgb = images[0, ..., :3].squeeze().cpu()
+            if DEBUG:
+                filename = "DEBUG_%s" % filename
+                Image.fromarray((rgb.detach().numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / filename
+                )
+            self.assertClose(rgb, image_ref, atol=0.015)
+
+        # Check grad exists.
+        grad_images = torch.randn_like(images)
+        images.backward(grad_images)
+        self.assertIsNotNone(verts.grad)
+        self.assertIsNotNone(rgb_feats.grad)
+
+    def test_simple_sphere_batched(self):
+        device = torch.device("cuda:0")
+        sphere_mesh = ico_sphere(1, device)
+        verts_padded = sphere_mesh.verts_padded()
+        verts_padded[..., 1] += 0.2
+        verts_padded[..., 0] += 0.2
+        pointclouds = Pointclouds(
+            points=verts_padded, features=torch.ones_like(verts_padded)
+        )
+        batch_size = 20
+        pointclouds = pointclouds.extend(batch_size)
+        R, T = look_at_view_transform(2.7, 0.0, 0.0)
+        cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
+        raster_settings = PointsRasterizationSettings(
+            image_size=256, radius=5e-2, points_per_pixel=1
+        )
+        rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
+        compositor = NormWeightedCompositor()
+        renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
+
+        # Load reference image
+        filename = "simple_pointcloud_sphere.png"
+        image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
+
+        images = renderer(pointclouds)
+        for i in range(batch_size):
+            rgb = images[i, ..., :3].squeeze().cpu()
+            if i == 0 and DEBUG:
+                filename = "DEBUG_%s" % filename
+                Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
+                    DATA_DIR / filename
+                )
+            self.assertClose(rgb, image_ref)