Convert from Pytorch3D NDC coordinates to grid_sample coordinates.

Summary: Implements a utility function to convert from 2D coordinates in Pytorch3D NDC space to the coordinates in grid_sample.

Reviewed By: shapovalov

Differential Revision: D33741394

fbshipit-source-id: 88981653356588fe646e6dea48fe7f7298738437

pytorch3d/renderer/__init__.py

@@ -70,7 +70,12 @@
     PulsarPointsRenderer,
     rasterize_points,
 )
-from .utils import TensorProperties, convert_to_tensors_and_broadcast
+from .utils import (
+    TensorProperties,
+    convert_to_tensors_and_broadcast,
+    ndc_to_grid_sample_coords,
+    ndc_grid_sample,
+)
 
 
 __all__ = [k for k in globals().keys() if not k.startswith("_")]

pytorch3d/renderer/utils.py

@@ -8,7 +8,7 @@
 import copy
 import inspect
 import warnings
-from typing import Any, Optional, Union
+from typing import Any, Optional, Union, Tuple
 
 import numpy as np
 import torch
@@ -350,3 +350,80 @@ def convert_to_tensors_and_broadcast(
         args_Nd.append(c.expand(*expand_sizes))
 
     return args_Nd
+
+
+def ndc_grid_sample(
+    input: torch.Tensor,
+    grid_ndc: torch.Tensor,
+    **grid_sample_kwargs,
+) -> torch.Tensor:
+    """
+    Samples a tensor `input` of shape `(B, dim, H, W)` at 2D locations
+    specified by a tensor `grid_ndc` of shape `(B, ..., 2)` using
+    the `torch.nn.functional.grid_sample` function.
+    `grid_ndc` is specified in PyTorch3D NDC coordinate frame.
+
+    Args:
+        input: The tensor of shape `(B, dim, H, W)` to be sampled.
+        grid_ndc: A tensor of shape `(B, ..., 2)` denoting the set of
+            2D locations at which `input` is sampled.
+            See [1] for a detailed description of the NDC coordinates.
+        grid_sample_kwargs: Additional arguments forwarded to the
+            `torch.nn.functional.grid_sample` call. See the corresponding
+            docstring for a listing of the corresponding arguments.
+
+    Returns:
+        sampled_input: A tensor of shape `(B, dim, ...)` containing the samples
+            of `input` at 2D locations `grid_ndc`.
+
+    References:
+        [1] https://pytorch3d.org/docs/cameras
+    """
+
+    batch, *spatial_size, pt_dim = grid_ndc.shape
+    if batch != input.shape[0]:
+        raise ValueError("'input' and 'grid_ndc' have to have the same batch size.")
+    if input.ndim != 4:
+        raise ValueError("'input' has to be a 4-dimensional Tensor.")
+    if pt_dim != 2:
+        raise ValueError("The last dimension of 'grid_ndc' has to be == 2.")
+
+    grid_ndc_flat = grid_ndc.reshape(batch, -1, 1, 2)
+
+    grid_flat = ndc_to_grid_sample_coords(grid_ndc_flat, input.shape[2:])
+
+    sampled_input_flat = torch.nn.functional.grid_sample(
+        input, grid_flat, **grid_sample_kwargs
+    )
+
+    sampled_input = sampled_input_flat.reshape([batch, input.shape[1], *spatial_size])
+
+    return sampled_input
+
+
+def ndc_to_grid_sample_coords(
+    xy_ndc: torch.Tensor,
+    image_size_hw: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Convert from the PyTorch3D's NDC coordinates to
+    `torch.nn.functional.grid_sampler`'s coordinates.
+
+    Args:
+        xy_ndc: Tensor of shape `(..., 2)` containing 2D points in the
+            PyTorch3D's NDC coordinates.
+        image_size_hw: A tuple `(image_height, image_width)` denoting the
+            height and width of the image tensor to sample.
+    Returns:
+        xy_grid_sample: Tensor of shape `(..., 2)` containing 2D points in the
+            `torch.nn.functional.grid_sample` coordinates.
+    """
+    if len(image_size_hw) != 2 or any(s <= 0 for s in image_size_hw):
+        raise ValueError("'image_size_hw' has to be a 2-tuple of positive integers")
+    aspect = min(image_size_hw) / max(image_size_hw)
+    xy_grid_sample = -xy_ndc  # first negate the coords
+    if image_size_hw[0] >= image_size_hw[1]:
+        xy_grid_sample[..., 1] *= aspect
+    else:
+        xy_grid_sample[..., 0] *= aspect
+    return xy_grid_sample

tests/test_rendering_utils.py

@@ -10,7 +10,20 @@
 import numpy as np
 import torch
 from common_testing import TestCaseMixin
-from pytorch3d.renderer.utils import TensorProperties
+from pytorch3d.ops import eyes
+from pytorch3d.renderer import (
+    PerspectiveCameras,
+    AlphaCompositor,
+    PointsRenderer,
+    PointsRasterizationSettings,
+    PointsRasterizer,
+)
+from pytorch3d.renderer.utils import (
+    TensorProperties,
+    ndc_to_grid_sample_coords,
+    ndc_grid_sample,
+)
+from pytorch3d.structures import Pointclouds
 
 
 # Example class for testing
@@ -96,3 +109,165 @@ def test_gather_props(self):
                 # the input.
                 self.assertClose(test_class_gathered.x[inds].mean(dim=0), x[i, ...])
                 self.assertClose(test_class_gathered.y[inds].mean(dim=0), y[i, ...])
+
+    def test_ndc_grid_sample_rendering(self):
+        """
+        Use PyTorch3D point renderer to render a colored point cloud, then
+        sample the image at the locations of the point projections with
+        `ndc_grid_sample`. Finally, assert that the sampled colors are equal to the
+        original point cloud colors.
+
+        Note that, in order to ensure correctness, we use a nearest-neighbor
+        assignment point renderer (i.e. no soft splatting).
+        """
+
+        # generate a bunch of 3D points on a regular grid lying in the z-plane
+        n_grid_pts = 10
+        grid_scale = 0.9
+        z_plane = 2.0
+        image_size = [128, 128]
+        point_radius = 0.015
+        n_pts = n_grid_pts * n_grid_pts
+        pts = torch.stack(
+            torch.meshgrid(
+                [torch.linspace(-grid_scale, grid_scale, n_grid_pts)] * 2, indexing="ij"
+            ),
+            dim=-1,
+        )
+        pts = torch.cat([pts, z_plane * torch.ones_like(pts[..., :1])], dim=-1)
+        pts = pts.reshape(1, n_pts, 3)
+
+        # color the points randomly
+        pts_colors = torch.rand(1, n_pts, 3)
+
+        # make trivial rendering cameras
+        cameras = PerspectiveCameras(
+            R=eyes(dim=3, N=1),
+            device=pts.device,
+            T=torch.zeros(1, 3, dtype=torch.float32, device=pts.device),
+        )
+
+        # render the point cloud
+        pcl = Pointclouds(points=pts, features=pts_colors)
+        renderer = NearestNeighborPointsRenderer(
+            rasterizer=PointsRasterizer(
+                cameras=cameras,
+                raster_settings=PointsRasterizationSettings(
+                    image_size=image_size,
+                    radius=point_radius,
+                    points_per_pixel=1,
+                ),
+            ),
+            compositor=AlphaCompositor(),
+        )
+        im_render = renderer(pcl)
+
+        # sample the render at projected pts
+        pts_proj = cameras.transform_points(pcl.points_padded())[..., :2]
+        pts_colors_sampled = ndc_grid_sample(
+            im_render,
+            pts_proj,
+            mode="nearest",
+            align_corners=False,
+        ).permute(0, 2, 1)
+
+        # assert that the samples are the same as original points
+        self.assertClose(pts_colors, pts_colors_sampled, atol=1e-4)
+
+    def test_ndc_to_grid_sample_coords(self):
+        """
+        Test the conversion from ndc to grid_sample coords by comparing
+        to known conversion results.
+        """
+
+        # square image tests
+        image_size_square = [100, 100]
+        xy_ndc_gs_square = torch.FloatTensor(
+            [
+                # 4 corners
+                [[-1.0, -1.0], [1.0, 1.0]],
+                [[1.0, 1.0], [-1.0, -1.0]],
+                [[1.0, -1.0], [-1.0, 1.0]],
+                [[1.0, 1.0], [-1.0, -1.0]],
+                # center
+                [[0.0, 0.0], [0.0, 0.0]],
+            ]
+        )
+
+        # non-batched version
+        for xy_ndc, xy_gs in xy_ndc_gs_square:
+            xy_gs_predicted = ndc_to_grid_sample_coords(
+                xy_ndc,
+                image_size_square,
+            )
+            self.assertClose(xy_gs_predicted, xy_gs)
+
+        # batched version
+        xy_ndc, xy_gs = xy_ndc_gs_square[:, 0], xy_ndc_gs_square[:, 1]
+        xy_gs_predicted = ndc_to_grid_sample_coords(
+            xy_ndc,
+            image_size_square,
+        )
+        self.assertClose(xy_gs_predicted, xy_gs)
+
+        # non-square image tests
+        image_size = [100, 200]
+        xy_ndc_gs = torch.FloatTensor(
+            [
+                # 4 corners
+                [[-2.0, -1.0], [1.0, 1.0]],
+                [[2.0, -1.0], [-1.0, 1.0]],
+                [[-2.0, 1.0], [1.0, -1.0]],
+                [[2.0, 1.0], [-1.0, -1.0]],
+                # center
+                [[0.0, 0.0], [0.0, 0.0]],
+                # non-corner points
+                [[4.0, 0.5], [-2.0, -0.5]],
+                [[1.0, -0.5], [-0.5, 0.5]],
+            ]
+        )
+
+        # check both H > W and W > H
+        for flip_axes in [False, True]:
+
+            # non-batched version
+            for xy_ndc, xy_gs in xy_ndc_gs:
+                xy_gs_predicted = ndc_to_grid_sample_coords(
+                    xy_ndc.flip(dims=(-1,)) if flip_axes else xy_ndc,
+                    list(reversed(image_size)) if flip_axes else image_size,
+                )
+                self.assertClose(
+                    xy_gs_predicted,
+                    xy_gs.flip(dims=(-1,)) if flip_axes else xy_gs,
+                )
+
+            # batched version
+            xy_ndc, xy_gs = xy_ndc_gs[:, 0], xy_ndc_gs[:, 1]
+            xy_gs_predicted = ndc_to_grid_sample_coords(
+                xy_ndc.flip(dims=(-1,)) if flip_axes else xy_ndc,
+                list(reversed(image_size)) if flip_axes else image_size,
+            )
+            self.assertClose(
+                xy_gs_predicted,
+                xy_gs.flip(dims=(-1,)) if flip_axes else xy_gs,
+            )
+
+
+class NearestNeighborPointsRenderer(PointsRenderer):
+    """
+    A class for rendering a batch of points by a trivial nearest
+    neighbor assignment.
+    """
+
+    def forward(self, point_clouds, **kwargs) -> torch.Tensor:
+        fragments = self.rasterizer(point_clouds, **kwargs)
+        # set all weights trivially to one
+        dists2 = fragments.dists.permute(0, 3, 1, 2)
+        weights = torch.ones_like(dists2)
+        images = self.compositor(
+            fragments.idx.long().permute(0, 3, 1, 2),
+            weights,
+            point_clouds.features_packed().permute(1, 0),
+            **kwargs,
+        )
+        return images