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Points2vols doc fixes

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Summary: Fixes to a couple of comments on points to volumes, make the mask work in round_points_to_volumes, and remove a duplicate rand calculation

Reviewed By: nikhilaravi

Differential Revision: D29522845

fbshipit-source-id: 86770ba37ef3942b909baf63fd73eed1399635b6
This commit is contained in:
Jeremy Reizenstein 2021-08-17 05:57:43 -07:00 committed by Facebook GitHub Bot
parent ae1387b523
commit 5491b46511
2 changed files with 13 additions and 9 deletions
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18
pytorch3d/ops/points_to_volumes.py
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@ -157,12 +157,15 @@ def add_points_features_to_volume_densities_features(
of its floating point coordinate. The weights are of its floating point coordinate. The weights are
determined using a trilinear interpolation scheme. determined using a trilinear interpolation scheme.
Trilinear splatting is fully differentiable w.r.t. all input arguments. Trilinear splatting is fully differentiable w.r.t. all input arguments.
mask: A binary mask of shape `(minibatch, N)` determining which 3D points
are going to be converted to the resulting volume.
Set to `None` if all points are valid.
min_weight: A scalar controlling the lowest possible total per-voxel min_weight: A scalar controlling the lowest possible total per-voxel
weight used to normalize the features accumulated in a voxel. weight used to normalize the features accumulated in a voxel.
Only active for `mode==trilinear`. Only active for `mode==trilinear`.
mask: A binary mask of shape `(minibatch, N)` determining which 3D points
are going to be converted to the resulting volume.
Set to `None` if all points are valid.
grid_sizes: `LongTensor` of shape (minibatch, 3) representing the
spatial resolutions of each of the the non-flattened `volumes` tensors,
or None to indicate the whole volume is used for every batch element.
Returns: Returns:
volume_features: Output volume of shape `(minibatch, feature_dim, D, H, W)` volume_features: Output volume of shape `(minibatch, feature_dim, D, H, W)`
volume_densities: Occupancy volume of shape `(minibatch, 1, D, H, W)` volume_densities: Occupancy volume of shape `(minibatch, 1, D, H, W)`
@ -284,13 +287,15 @@ def splat_points_to_volumes(
volume_features: Batch of input *flattened* feature volumes volume_features: Batch of input *flattened* feature volumes
of shape `(minibatch, feature_dim, N_voxels)` of shape `(minibatch, feature_dim, N_voxels)`
volume_densities: Batch of input *flattened* feature volume densities volume_densities: Batch of input *flattened* feature volume densities
of shape `(minibatch, 1, N_voxels)`. Each voxel should of shape `(minibatch, N_voxels, 1)`. Each voxel should
contain a non-negative number corresponding to its contain a non-negative number corresponding to its
opaqueness (the higher, the less transparent). opaqueness (the higher, the less transparent).
grid_sizes: `LongTensor` of shape (minibatch, 3) representing the grid_sizes: `LongTensor` of shape (minibatch, 3) representing the
spatial resolutions of each of the the non-flattened `volumes` tensors. spatial resolutions of each of the the non-flattened `volumes` tensors.
Note that the following has to hold: Note that the following has to hold:
`torch.prod(grid_sizes, dim=1)==N_voxels` `torch.prod(grid_sizes, dim=1)==N_voxels`
min_weight: A scalar controlling the lowest possible total per-voxel
weight used to normalize the features accumulated in a voxel.
mask: A binary mask of shape `(minibatch, N)` determining which 3D points mask: A binary mask of shape `(minibatch, N)` determining which 3D points
are going to be converted to the resulting volume. are going to be converted to the resulting volume.
Set to `None` if all points are valid. Set to `None` if all points are valid.
@ -457,9 +462,6 @@ def round_points_to_volumes(
# split into separate coordinate vectors # split into separate coordinate vectors
X, Y, Z = XYZ.split(1, dim=2) X, Y, Z = XYZ.split(1, dim=2)
# get random indices for the purpose of adding out-of-bounds values
rand_idx = X.new_zeros(X.shape).random_(0, n_voxels)
# valid - binary indicators of votes that fall into the volume # valid - binary indicators of votes that fall into the volume
grid_sizes = grid_sizes.type_as(XYZ) grid_sizes = grid_sizes.type_as(XYZ)
valid = ( valid = (
@ -470,6 +472,8 @@ def round_points_to_volumes(
* (0 <= Z) * (0 <= Z)
* (Z < grid_sizes_xyz[:, None, 2:3]) * (Z < grid_sizes_xyz[:, None, 2:3])
).long() ).long()
if mask is not None:
valid = valid * mask[:, :, None].long()
# get random indices for the purpose of adding out-of-bounds values # get random indices for the purpose of adding out-of-bounds values
rand_idx = valid.new_zeros(X.shape).random_(0, n_voxels) rand_idx = valid.new_zeros(X.shape).random_(0, n_voxels)

4
tests/test_points_to_volumes.py
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@ -30,8 +30,8 @@ def init_cube_point_cloud(
batch_size: int = 10, n_points: int = 100000, rotate_y: bool = True batch_size: int = 10, n_points: int = 100000, rotate_y: bool = True
): ):
""" """
Generate a random point cloud of `n_points` whose points of Generate a random point cloud of `n_points` whose points
which are sampled from faces of a 3D cube. are sampled from faces of a 3D cube.
""" """
# create the cube mesh batch_size times # create the cube mesh batch_size times