pulsar integration.

Summary:
This diff integrates the pulsar renderer source code into PyTorch3D as an alternative backend for the PyTorch3D point renderer. This diff is the first of a series of three diffs to complete that migration and focuses on the packaging and integration of the source code.

For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder `docs/examples`.

Tasks addressed in the following diffs:
* Add the PyTorch3D interface,
* Add notebook examples and documentation (or adapt the existing ones to feature both interfaces).

Reviewed By: nikhilaravi

Differential Revision: D23947736

fbshipit-source-id: a5e77b53e6750334db22aefa89b4c079cda1b443

tests/pulsar/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.

tests/pulsar/create_multiview.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Create multiview data."""
+import sys
+from os import path
+
+
+# Making sure you can run this, even if pulsar hasn't been installed yet.
+sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
+
+
+def create_multiview():
+    """Test multiview optimization."""
+    from pytorch3d.renderer.points.pulsar import Renderer
+    import torch
+    from torch import nn
+    import imageio
+    from torch.autograd import Variable
+
+    # import cv2
+    # import skvideo.io
+    import numpy as np
+
+    # Constructor.
+    n_points = 10
+    width = 1000
+    height = 1000
+
+    class Model(nn.Module):
+        """A dummy model to test the integration into a stacked model."""
+
+        def __init__(self):
+            super(Model, self).__init__()
+            self.gamma = 0.1
+            self.renderer = Renderer(width, height, n_points)
+
+        def forward(self, vp, vc, vr, cam_params):
+            # self.gamma *= 0.995
+            # print("gamma: ", self.gamma)
+            return self.renderer.forward(vp, vc, vr, cam_params, self.gamma, 45.0)
+
+    # Generate sample data.
+    torch.manual_seed(1)
+    vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
+    vert_pos[:, 2] += 25.0
+    vert_pos[:, :2] -= 5.0
+    # print(vert_pos[0])
+    vert_col = torch.rand(n_points, 3, dtype=torch.float32)
+    vert_rad = torch.rand(n_points, dtype=torch.float32)
+
+    # Distortion.
+    # vert_pos[:, 1] += 0.5
+    vert_col *= 0.5
+    # vert_rad *= 0.7
+
+    for device in [torch.device("cuda")]:
+        model = Model().to(device)
+        vert_pos = vert_pos.to(device)
+        vert_col = vert_col.to(device)
+        vert_rad = vert_rad.to(device)
+        for angle_idx, angle in enumerate([-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5]):
+            vert_pos_v = Variable(vert_pos, requires_grad=False)
+            vert_col_v = Variable(vert_col, requires_grad=False)
+            vert_rad_v = Variable(vert_rad, requires_grad=False)
+            cam_params = torch.tensor(
+                [
+                    np.sin(angle) * 35.0,
+                    0.0,
+                    30.0 - np.cos(angle) * 35.0,
+                    0.0,
+                    -angle,
+                    0.0,
+                    5.0,
+                    2.0,
+                ],
+                dtype=torch.float32,
+            ).to(device)
+            cam_params_v = Variable(cam_params, requires_grad=False)
+            result = model.forward(vert_pos_v, vert_col_v, vert_rad_v, cam_params_v)
+            result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+            imageio.imsave(
+                "reference/examples_TestRenderer_test_multiview_%d.png" % (angle_idx),
+                result_im,
+            )
+
+
+if __name__ == "__main__":
+    create_multiview()

tests/pulsar/test_channels.py

@@ -0,0 +1,149 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Test number of channels."""
+import logging
+import sys
+import unittest
+from os import path
+
+import torch
+
+
+# fmt: off
+# Make the mixin available.
+sys.path.insert(0, path.join(path.dirname(__file__), ".."))
+from common_testing import TestCaseMixin  # isort:skip  # noqa: E402
+# fmt: on
+
+
+sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
+devices = [torch.device("cuda"), torch.device("cpu")]
+
+
+class TestChannels(TestCaseMixin, unittest.TestCase):
+    """Test different numbers of channels."""
+
+    def test_basic(self):
+        """Basic forward test."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+        import torch
+
+        n_points = 10
+        width = 1_000
+        height = 1_000
+        renderer_1 = Renderer(width, height, n_points, n_channels=1)
+        renderer_3 = Renderer(width, height, n_points, n_channels=3)
+        renderer_8 = Renderer(width, height, n_points, n_channels=8)
+        # Generate sample data.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        vert_col = torch.rand(n_points, 8, dtype=torch.float32)
+        vert_rad = torch.rand(n_points, dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer_1 = renderer_1.to(device)
+            renderer_3 = renderer_3.to(device)
+            renderer_8 = renderer_8.to(device)
+            result_1 = (
+                renderer_1.forward(
+                    vert_pos,
+                    vert_col[:, :1],
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_1 = (
+                renderer_1.forward(
+                    vert_pos,
+                    vert_col[:, :1],
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            result_3 = (
+                renderer_3.forward(
+                    vert_pos,
+                    vert_col[:, :3],
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_3 = (
+                renderer_3.forward(
+                    vert_pos,
+                    vert_col[:, :3],
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            result_8 = (
+                renderer_8.forward(
+                    vert_pos,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_8 = (
+                renderer_8.forward(
+                    vert_pos,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            self.assertClose(result_1, result_3[:, :, :1])
+            self.assertClose(result_3, result_8[:, :, :3])
+            self.assertClose(hits_1, hits_3)
+            self.assertClose(hits_8, hits_3)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    unittest.main()

tests/pulsar/test_depth.py

@@ -0,0 +1,97 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Test the sorting of the closest spheres."""
+import logging
+import os
+import sys
+import unittest
+from os import path
+
+import imageio
+import numpy as np
+import torch
+
+
+# fmt: off
+# Make the mixin available.
+sys.path.insert(0, path.join(path.dirname(__file__), ".."))
+from common_testing import TestCaseMixin  # isort:skip  # noqa: E402
+# fmt: on
+
+# Making sure you can run this, even if pulsar hasn't been installed yet.
+sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
+
+devices = [torch.device("cuda"), torch.device("cpu")]
+IN_REF_FP = path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
+OUT_REF_FP = path.join(path.dirname(__file__), "reference", "nr0000-out.pth")
+
+
+class TestDepth(TestCaseMixin, unittest.TestCase):
+    """Test different numbers of channels."""
+
+    def test_basic(self):
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        for device in devices:
+            gamma = 1e-5
+            max_depth = 15.0
+            min_depth = 5.0
+            renderer = Renderer(
+                256,
+                256,
+                10000,
+                orthogonal_projection=True,
+                right_handed_system=False,
+                n_channels=1,
+            ).to(device)
+            data = torch.load(IN_REF_FP, map_location="cpu")
+            # data["pos"] = torch.rand_like(data["pos"])
+            # data["pos"][:, 0] = data["pos"][:, 0] * 2. - 1.
+            # data["pos"][:, 1] = data["pos"][:, 1] * 2. - 1.
+            # data["pos"][:, 2] = data["pos"][:, 2] + 9.5
+            result, result_info = renderer.forward(
+                data["pos"].to(device),
+                data["col"].to(device),
+                data["rad"].to(device),
+                data["cam_params"].to(device),
+                gamma,
+                min_depth=min_depth,
+                max_depth=max_depth,
+                return_forward_info=True,
+                bg_col=torch.zeros(1, device=device, dtype=torch.float32),
+                percent_allowed_difference=0.01,
+            )
+            sphere_ids = Renderer.sphere_ids_from_result_info_nograd(result_info)
+            depth_map = Renderer.depth_map_from_result_info_nograd(result_info)
+            depth_vis = (depth_map - depth_map[depth_map > 0].min()) * 200 / (
+                depth_map.max() - depth_map[depth_map > 0.0].min()
+            ) + 50
+            if not os.environ.get("FB_TEST", False):
+                imageio.imwrite(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_depth_test_basic_depth.png",
+                    ),
+                    depth_vis.cpu().numpy().astype(np.uint8),
+                )
+            # torch.save(
+            #     data, path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
+            # )
+            # torch.save(
+            #     {"sphere_ids": sphere_ids, "depth_map": depth_map},
+            #     path.join(path.dirname(__file__), "reference", "nr0000-out.pth"),
+            # )
+            # sys.exit(0)
+            reference = torch.load(OUT_REF_FP, map_location="cpu")
+            self.assertTrue(
+                torch.sum(
+                    reference["sphere_ids"][..., 0].to(device) == sphere_ids[..., 0]
+                )
+                > 65530
+            )
+            self.assertClose(reference["depth_map"].to(device), depth_map)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    unittest.main()

tests/pulsar/test_forward.py

@@ -0,0 +1,353 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Basic rendering test."""
+import logging
+import os
+import sys
+import unittest
+from os import path
+
+import imageio
+import numpy as np
+import torch
+
+
+# Making sure you can run this, even if pulsar hasn't been installed yet.
+sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
+LOGGER = logging.getLogger(__name__)
+devices = [torch.device("cuda"), torch.device("cpu")]
+
+
+class TestForward(unittest.TestCase):
+    """Rendering tests."""
+
+    def test_bg_weight(self):
+        """Test background reweighting."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        LOGGER.info("Setting up rendering test for 3 channels...")
+        n_points = 1
+        width = 1_000
+        height = 1_000
+        renderer = Renderer(width, height, n_points, background_normalized_depth=0.999)
+        vert_pos = torch.tensor([[0.0, 0.0, 25.0]], dtype=torch.float32)
+        vert_col = torch.tensor([[0.3, 0.5, 0.7]], dtype=torch.float32)
+        vert_rad = torch.tensor([1.0], dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer = renderer.to(device)
+            LOGGER.info("Rendering...")
+            # Measurements.
+            result = renderer.forward(
+                vert_pos, vert_col, vert_rad, cam_params, 1.0e-1, 45.0
+            )
+            hits = renderer.forward(
+                vert_pos,
+                vert_col,
+                vert_rad,
+                cam_params,
+                1.0e-1,
+                45.0,
+                percent_allowed_difference=0.01,
+                mode=1,
+            )
+            if not os.environ.get("FB_TEST", False):
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_bg_weight.png",
+                    ),
+                    (result * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_bg_weight_hits.png",
+                    ),
+                    (hits * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+            self.assertEqual(hits[500, 500, 0].item(), 1.0)
+            self.assertTrue(
+                np.allclose(
+                    result[500, 500, :].cpu().numpy(),
+                    [1.0, 1.0, 1.0],
+                    rtol=1e-2,
+                    atol=1e-2,
+                )
+            )
+
+    def test_basic_3chan(self):
+        """Test rendering one image with one sphere, 3 channels."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        LOGGER.info("Setting up rendering test for 3 channels...")
+        n_points = 1
+        width = 1_000
+        height = 1_000
+        renderer = Renderer(width, height, n_points)
+        vert_pos = torch.tensor([[0.0, 0.0, 25.0]], dtype=torch.float32)
+        vert_col = torch.tensor([[0.3, 0.5, 0.7]], dtype=torch.float32)
+        vert_rad = torch.tensor([1.0], dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer = renderer.to(device)
+            LOGGER.info("Rendering...")
+            # Measurements.
+            result = renderer.forward(
+                vert_pos, vert_col, vert_rad, cam_params, 1.0e-1, 45.0
+            )
+            hits = renderer.forward(
+                vert_pos,
+                vert_col,
+                vert_rad,
+                cam_params,
+                1.0e-1,
+                45.0,
+                percent_allowed_difference=0.01,
+                mode=1,
+            )
+            if not os.environ.get("FB_TEST", False):
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_basic_3chan.png",
+                    ),
+                    (result * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_basic_3chan_hits.png",
+                    ),
+                    (hits * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+            self.assertEqual(hits[500, 500, 0].item(), 1.0)
+            self.assertTrue(
+                np.allclose(
+                    result[500, 500, :].cpu().numpy(),
+                    [0.3, 0.5, 0.7],
+                    rtol=1e-2,
+                    atol=1e-2,
+                )
+            )
+
+    def test_basic_1chan(self):
+        """Test rendering one image with one sphere, 1 channel."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        LOGGER.info("Setting up rendering test for 1 channel...")
+        n_points = 1
+        width = 1_000
+        height = 1_000
+        renderer = Renderer(width, height, n_points, n_channels=1)
+        vert_pos = torch.tensor([[0.0, 0.0, 25.0]], dtype=torch.float32)
+        vert_col = torch.tensor([[0.3]], dtype=torch.float32)
+        vert_rad = torch.tensor([1.0], dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer = renderer.to(device)
+            LOGGER.info("Rendering...")
+            # Measurements.
+            result = renderer.forward(
+                vert_pos, vert_col, vert_rad, cam_params, 1.0e-1, 45.0
+            )
+            hits = renderer.forward(
+                vert_pos,
+                vert_col,
+                vert_rad,
+                cam_params,
+                1.0e-1,
+                45.0,
+                percent_allowed_difference=0.01,
+                mode=1,
+            )
+            if not os.environ.get("FB_TEST", False):
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_basic_1chan.png",
+                    ),
+                    (result * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_basic_1chan_hits.png",
+                    ),
+                    (hits * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+            self.assertEqual(hits[500, 500, 0].item(), 1.0)
+            self.assertTrue(
+                np.allclose(
+                    result[500, 500, :].cpu().numpy(), [0.3], rtol=1e-2, atol=1e-2
+                )
+            )
+
+    def test_basic_8chan(self):
+        """Test rendering one image with one sphere, 8 channels."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        LOGGER.info("Setting up rendering test for 8 channels...")
+        n_points = 1
+        width = 1_000
+        height = 1_000
+        renderer = Renderer(width, height, n_points, n_channels=8)
+        vert_pos = torch.tensor([[0.0, 0.0, 25.0]], dtype=torch.float32)
+        vert_col = torch.tensor(
+            [[1.0, 1.0, 1.0, 1.0, 1.0, 0.3, 0.5, 0.7]], dtype=torch.float32
+        )
+        vert_rad = torch.tensor([1.0], dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer = renderer.to(device)
+            LOGGER.info("Rendering...")
+            # Measurements.
+            result = renderer.forward(
+                vert_pos, vert_col, vert_rad, cam_params, 1.0e-1, 45.0
+            )
+            hits = renderer.forward(
+                vert_pos,
+                vert_col,
+                vert_rad,
+                cam_params,
+                1.0e-1,
+                45.0,
+                percent_allowed_difference=0.01,
+                mode=1,
+            )
+            if not os.environ.get("FB_TEST", False):
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_basic_8chan.png",
+                    ),
+                    (result[:, :, 5:8] * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_basic_8chan_hits.png",
+                    ),
+                    (hits * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+            self.assertEqual(hits[500, 500, 0].item(), 1.0)
+            self.assertTrue(
+                np.allclose(
+                    result[500, 500, 5:8].cpu().numpy(),
+                    [0.3, 0.5, 0.7],
+                    rtol=1e-2,
+                    atol=1e-2,
+                )
+            )
+            self.assertTrue(
+                np.allclose(
+                    result[500, 500, :5].cpu().numpy(), 1.0, rtol=1e-2, atol=1e-2
+                )
+            )
+
+    def test_principal_point(self):
+        """Test shifting the principal point."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        LOGGER.info("Setting up rendering test for shifted principal point...")
+        n_points = 1
+        width = 1_000
+        height = 1_000
+        renderer = Renderer(width, height, n_points, n_channels=1)
+        vert_pos = torch.tensor([[0.0, 0.0, 25.0]], dtype=torch.float32)
+        vert_col = torch.tensor([[0.0]], dtype=torch.float32)
+        vert_rad = torch.tensor([1.0], dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0, 0.0, 0.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            cam_params[-2] = -250.0
+            cam_params[-1] = -250.0
+            renderer = renderer.to(device)
+            LOGGER.info("Rendering...")
+            # Measurements.
+            result = renderer.forward(
+                vert_pos, vert_col, vert_rad, cam_params, 1.0e-1, 45.0
+            )
+            if not os.environ.get("FB_TEST", False):
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_principal_point.png",
+                    ),
+                    (result * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+            self.assertTrue(
+                np.allclose(
+                    result[750, 750, :].cpu().numpy(), [0.0], rtol=1e-2, atol=1e-2
+                )
+            )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            cam_params[-2] = 250.0
+            cam_params[-1] = 250.0
+            renderer = renderer.to(device)
+            LOGGER.info("Rendering...")
+            # Measurements.
+            result = renderer.forward(
+                vert_pos, vert_col, vert_rad, cam_params, 1.0e-1, 45.0
+            )
+            if not os.environ.get("FB_TEST", False):
+                imageio.imsave(
+                    path.join(
+                        path.dirname(__file__),
+                        "test_out",
+                        "test_forward_TestForward_test_principal_point.png",
+                    ),
+                    (result * 255.0).cpu().to(torch.uint8).numpy(),
+                )
+            self.assertTrue(
+                np.allclose(
+                    result[250, 250, :].cpu().numpy(), [0.0], rtol=1e-2, atol=1e-2
+                )
+            )
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("pulsar.renderer").setLevel(logging.WARN)
+    unittest.main()

tests/pulsar/test_hands.py

@@ -0,0 +1,120 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Test right hand/left hand system compatibility."""
+import logging
+import sys
+import unittest
+from os import path
+
+import torch
+
+
+# fmt: off
+# Make the mixin available.
+sys.path.insert(0, path.join(path.dirname(__file__), ".."))
+from common_testing import TestCaseMixin  # isort:skip  # noqa: E402
+# fmt: on
+
+
+# Making sure you can run this, even if pulsar hasn't been installed yet.
+sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
+devices = [torch.device("cuda"), torch.device("cpu")]
+
+
+class TestHands(TestCaseMixin, unittest.TestCase):
+    """Test right hand/left hand system compatibility."""
+
+    def test_basic(self):
+        """Basic forward test."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        n_points = 10
+        width = 1000
+        height = 1000
+        renderer_left = Renderer(width, height, n_points, right_handed_system=False)
+        renderer_right = Renderer(width, height, n_points, right_handed_system=True)
+        # Generate sample data.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        vert_pos_neg = vert_pos.clone()
+        vert_pos_neg[:, 2] *= -1.0
+        vert_col = torch.rand(n_points, 3, dtype=torch.float32)
+        vert_rad = torch.rand(n_points, dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_pos_neg = vert_pos_neg.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer_left = renderer_left.to(device)
+            renderer_right = renderer_right.to(device)
+            result_left = (
+                renderer_left.forward(
+                    vert_pos,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_left = (
+                renderer_left.forward(
+                    vert_pos,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            result_right = (
+                renderer_right.forward(
+                    vert_pos_neg,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_right = (
+                renderer_right.forward(
+                    vert_pos_neg,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            self.assertClose(result_left, result_right)
+            self.assertClose(hits_left, hits_right)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("pulsar.renderer").setLevel(logging.WARN)
+    unittest.main()

tests/pulsar/test_ortho.py

@@ -0,0 +1,126 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Tests for the orthogonal projection."""
+import logging
+import sys
+import unittest
+from os import path
+
+import numpy as np
+import torch
+
+
+# Making sure you can run this, even if pulsar hasn't been installed yet.
+sys.path.insert(0, path.join(path.dirname(__file__), ".."))
+devices = [torch.device("cuda"), torch.device("cpu")]
+
+
+class TestOrtho(unittest.TestCase):
+    """Test the orthogonal projection."""
+
+    def test_basic(self):
+        """Basic forward test of the orthogonal projection."""
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        n_points = 10
+        width = 1000
+        height = 1000
+        renderer_left = Renderer(
+            width,
+            height,
+            n_points,
+            right_handed_system=False,
+            orthogonal_projection=True,
+        )
+        renderer_right = Renderer(
+            width,
+            height,
+            n_points,
+            right_handed_system=True,
+            orthogonal_projection=True,
+        )
+        # Generate sample data.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        vert_pos_neg = vert_pos.clone()
+        vert_pos_neg[:, 2] *= -1.0
+        vert_col = torch.rand(n_points, 3, dtype=torch.float32)
+        vert_rad = torch.rand(n_points, dtype=torch.float32)
+        cam_params = torch.tensor(
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 20.0], dtype=torch.float32
+        )
+        for device in devices:
+            vert_pos = vert_pos.to(device)
+            vert_pos_neg = vert_pos_neg.to(device)
+            vert_col = vert_col.to(device)
+            vert_rad = vert_rad.to(device)
+            cam_params = cam_params.to(device)
+            renderer_left = renderer_left.to(device)
+            renderer_right = renderer_right.to(device)
+            result_left = (
+                renderer_left.forward(
+                    vert_pos,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_left = (
+                renderer_left.forward(
+                    vert_pos,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            result_right = (
+                renderer_right.forward(
+                    vert_pos_neg,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            hits_right = (
+                renderer_right.forward(
+                    vert_pos_neg,
+                    vert_col,
+                    vert_rad,
+                    cam_params,
+                    1.0e-1,
+                    45.0,
+                    percent_allowed_difference=0.01,
+                    mode=1,
+                )
+                .cpu()
+                .detach()
+                .numpy()
+            )
+            self.assertTrue(np.allclose(result_left, result_right))
+            self.assertTrue(np.allclose(hits_left, hits_right))
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("pulsar.renderer").setLevel(logging.WARN)
+    unittest.main()

tests/pulsar/test_small_spheres.py

@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+"""Test right hand/left hand system compatibility."""
+import sys
+import unittest
+from os import path
+
+import numpy as np
+import torch
+from torch import nn
+
+
+sys.path.insert(0, path.join(path.dirname(__file__), ".."))
+devices = [torch.device("cuda"), torch.device("cpu")]
+
+
+n_points = 10
+width = 1_000
+height = 1_000
+
+
+class SceneModel(nn.Module):
+    """A simple model to demonstrate use in Modules."""
+
+    def __init__(self):
+        super(SceneModel, self).__init__()
+        from pytorch3d.renderer.points.pulsar import Renderer
+
+        self.gamma = 1.0
+        # Points.
+        torch.manual_seed(1)
+        vert_pos = torch.rand((1, n_points, 3), dtype=torch.float32) * 10.0
+        vert_pos[:, :, 2] += 25.0
+        vert_pos[:, :, :2] -= 5.0
+        self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False))
+        self.register_parameter(
+            "vert_col",
+            nn.Parameter(
+                torch.zeros(1, n_points, 3, dtype=torch.float32), requires_grad=True
+            ),
+        )
+        self.register_parameter(
+            "vert_rad",
+            nn.Parameter(
+                torch.ones(1, n_points, dtype=torch.float32) * 0.001,
+                requires_grad=False,
+            ),
+        )
+        self.register_parameter(
+            "vert_opy",
+            nn.Parameter(
+                torch.ones(1, n_points, dtype=torch.float32), requires_grad=False
+            ),
+        )
+        self.register_buffer(
+            "cam_params",
+            torch.tensor(
+                [
+                    [
+                        np.sin(angle) * 35.0,
+                        0.0,
+                        30.0 - np.cos(angle) * 35.0,
+                        0.0,
+                        -angle,
+                        0.0,
+                        5.0,
+                        2.0,
+                    ]
+                    for angle in [-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5]
+                ],
+                dtype=torch.float32,
+            ),
+        )
+        self.renderer = Renderer(width, height, n_points)
+
+    def forward(self, cam=None):
+        if cam is None:
+            cam = self.cam_params
+            n_views = 8
+        else:
+            n_views = 1
+        return self.renderer.forward(
+            self.vert_pos.expand(n_views, -1, -1),
+            self.vert_col.expand(n_views, -1, -1),
+            self.vert_rad.expand(n_views, -1),
+            cam,
+            self.gamma,
+            45.0,
+            return_forward_info=True,
+        )
+
+
+class TestSmallSpheres(unittest.TestCase):
+    """Test small sphere rendering and gradients."""
+
+    def test_basic(self):
+        for device in devices:
+            # Set up model.
+            model = SceneModel().to(device)
+            angle = 0.0
+            for _ in range(50):
+                cam_control = torch.tensor(
+                    [
+                        [
+                            np.sin(angle) * 35.0,
+                            0.0,
+                            30.0 - np.cos(angle) * 35.0,
+                            0.0,
+                            -angle,
+                            0.0,
+                            5.0,
+                            2.0,
+                        ]
+                    ],
+                    dtype=torch.float32,
+                ).to(device)
+                result, forw_info = model(cam=cam_control)
+                sphere_ids = model.renderer.sphere_ids_from_result_info_nograd(
+                    forw_info
+                )
+                # Assert all spheres are rendered.
+                for idx in range(n_points):
+                    self.assertTrue(
+                        (sphere_ids == idx).sum() > 0, "Sphere ID %d missing!" % (idx)
+                    )
+                # Visualize.
+                # result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+                # cv2.imshow("res", result_im[0, :, :, ::-1])
+                # cv2.waitKey(0)
+                # Back-propagate some dummy gradients.
+                loss = ((result - torch.ones_like(result)).abs()).sum()
+                loss.backward()
+                # Now check whether the gradient arrives at every sphere.
+                self.assertTrue(torch.all(model.vert_col.grad[:, :, 0].abs() > 0.0))
+                angle += 0.15
+
+
+if __name__ == "__main__":
+    unittest.main()