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d0e86f6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
def sample_point_cloud(
point_cloud: torch.Tensor,
n_pts: int,
seed: int = 44,
) -> torch.Tensor:
"""
Subsample points from a temporal point cloud sequence.
Uses a single random permutation applied consistently across all timesteps
to maintain point correspondence.
Args:
point_cloud: (T, N, C) point clouds over T timesteps.
n_pts: Number of points to sample.
seed: Random seed for reproducibility.
Returns:
(T, n_pts, C) subsampled point clouds.
"""
n_pts_src = point_cloud.shape[1]
if n_pts_src <= n_pts:
return point_cloud
rng = np.random.RandomState(seed=seed)
indices = torch.from_numpy(rng.permutation(n_pts_src)[:n_pts]).long()
return point_cloud[:, indices]
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