79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
import torch
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from torch.autograd import Function
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from . import furthest_point_sample_ext
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class FurthestPointSampling(Function):
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"""Furthest Point Sampling.
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Uses iterative furthest point sampling to select a set of features whose
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corresponding points have the furthest distance.
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"""
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@staticmethod
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def forward(ctx, points_xyz: torch.Tensor, num_points: int) -> torch.Tensor:
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"""forward.
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Args:
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points_xyz (Tensor): (B, N, 3) where N > num_points.
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num_points (int): Number of points in the sampled set.
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Returns:
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Tensor: (B, num_points) indices of the sampled points.
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"""
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assert points_xyz.is_contiguous()
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B, N = points_xyz.size()[:2]
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output = torch.cuda.IntTensor(B, num_points)
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temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
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furthest_point_sample_ext.furthest_point_sampling_wrapper(
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B, N, num_points, points_xyz, temp, output
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)
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ctx.mark_non_differentiable(output)
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return output
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@staticmethod
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def backward(xyz, a=None):
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return None, None
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class FurthestPointSamplingWithDist(Function):
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"""Furthest Point Sampling With Distance.
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Uses iterative furthest point sampling to select a set of features whose
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corresponding points have the furthest distance.
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"""
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@staticmethod
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def forward(ctx, points_dist: torch.Tensor, num_points: int) -> torch.Tensor:
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"""forward.
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Args:
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points_dist (Tensor): (B, N, N) Distance between each point pair.
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num_points (int): Number of points in the sampled set.
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Returns:
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Tensor: (B, num_points) indices of the sampled points.
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"""
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assert points_dist.is_contiguous()
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B, N, _ = points_dist.size()
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output = points_dist.new_zeros([B, num_points], dtype=torch.int32)
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temp = points_dist.new_zeros([B, N]).fill_(1e10)
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furthest_point_sample_ext.furthest_point_sampling_with_dist_wrapper(
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B, N, num_points, points_dist, temp, output
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)
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ctx.mark_non_differentiable(output)
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return output
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@staticmethod
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def backward(xyz, a=None):
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return None, None
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furthest_point_sample = FurthestPointSampling.apply
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furthest_point_sample_with_dist = FurthestPointSamplingWithDist.apply
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