pulsar interface unification.

Summary:
This diff builds on top of the `pulsar integration` diff to provide a unified interface for the existing PyTorch3D point renderer and Pulsar. 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.

The unified interfaces are completely consistent. Switching the render backend is as easy as using `renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)` instead of `renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)` and adding the `gamma` parameter to the forward function. All PyTorch3D camera types are supported as far as possible; keyword arguments are properly forwarded to the camera. The `PerspectiveCamera` and `OrthographicCamera` require znear and zfar as additional parameters for the forward pass.

Reviewed By: nikhilaravi

Differential Revision: D21421443

fbshipit-source-id: 4aa0a83a419592d9a0bb5d62486a1cdea9d73ce6

pytorch3d/renderer/points/__init__.py

@@ -1,6 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from .compositor import AlphaCompositor, NormWeightedCompositor
+from .pulsar.unified import PulsarPointsRenderer
 from .rasterize_points import rasterize_points
 from .rasterizer import PointsRasterizationSettings, PointsRasterizer
 from .renderer import PointsRenderer

pytorch3d/renderer/points/pulsar/renderer.py

@@ -369,6 +369,7 @@ def _transform_cam_params(
         height: int,
         orthogonal: bool,
         right_handed: bool,
+        first_R_then_T: bool = False,
     ) -> Tuple[
         torch.Tensor,
         torch.Tensor,
@@ -401,6 +402,8 @@ def _transform_cam_params(
                   (does not use focal length).
             * right_handed: bool, whether to use a right handed system
                   (negative z in camera direction).
+            * first_R_then_T: bool, whether to first rotate, then translate
+                  the camera (PyTorch3D convention).
 
         Returns:
             * pos_vec: the position vector in 3D,
@@ -460,16 +463,18 @@ def _transform_cam_params(
         # Always get quadratic pixels.
         pixel_size_x = sensor_size_x / float(width)
         sensor_size_y = height * pixel_size_x
+        if continuous_rep:
+            rot_mat = rotation_6d_to_matrix(rot_vec)
+        else:
+            rot_mat = axis_angle_to_matrix(rot_vec)
+        if first_R_then_T:
+            pos_vec = torch.matmul(rot_mat, pos_vec[..., None])[:, :, 0]
         LOGGER.debug(
             "Camera position: %s, rotation: %s. Focal length: %s.",
             str(pos_vec),
             str(rot_vec),
             str(focal_length),
         )
-        if continuous_rep:
-            rot_mat = rotation_6d_to_matrix(rot_vec)
-        else:
-            rot_mat = axis_angle_to_matrix(rot_vec)
         sensor_dir_x = torch.matmul(
             rot_mat,
             torch.tensor(
@@ -576,6 +581,7 @@ def forward(
         max_n_hits: int = _C.MAX_UINT,
         mode: int = 0,
         return_forward_info: bool = False,
+        first_R_then_T: bool = False,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
         """
         Rendering pass to create an image from the provided spheres and camera
@@ -616,6 +622,8 @@ def forward(
                 the float encoded integer index of a sphere and its weight. They are the
                 five spheres with the highest color contribution to this pixel color,
                 ordered descending. Default: False.
+            * first_R_then_T: bool, whether to first apply rotation to the camera,
+                then translation (PyTorch3D convention). Default: False.
 
         Returns:
             * image: [Bx]HxWx3 float tensor with the resulting image.
@@ -638,6 +646,7 @@ def forward(
             self._renderer.height,
             self._renderer.orthogonal,
             self._renderer.right_handed,
+            first_R_then_T=first_R_then_T,
         )
         if (
             focal_lengths.min().item() > 0.0