examples and docs.

Summary: This diff updates the documentation and tutorials with information about the new pulsar backend. 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.

Reviewed By: nikhilaravi

Differential Revision: D24498129

fbshipit-source-id: e312b0169a72b13590df6e4db36bfe6190d742f9

docs/examples/pulsar_optimization.py

@@ -8,6 +8,8 @@ The scene is initialized with random spheres. Gradient-based
 optimization is used to converge towards a faithful
 scene representation.
 """
+import math
+
 import cv2
 import imageio
 import numpy as np
@@ -58,11 +60,15 @@ class SceneModel(nn.Module):
         )
         self.register_buffer(
             "cam_params",
-            torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32),
+            torch.tensor(
+                [0.0, 0.0, 0.0, 0.0, math.pi, 0.0, 5.0, 2.0], dtype=torch.float32
+            ),
         )
         # The volumetric optimization works better with a higher number of tracked
         # intersections per ray.
-        self.renderer = Renderer(width, height, n_points, n_track=32)
+        self.renderer = Renderer(
+            width, height, n_points, n_track=32, right_handed_system=True
+        )
 
     def forward(self):
         return self.renderer.forward(
@@ -81,7 +87,7 @@ ref = (
     torch.from_numpy(
         imageio.imread(
             "../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png"
-        )
+        )[:, ::-1, :].copy()
     ).to(torch.float32)
     / 255.0
 ).to(device)