def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
"""
Uses iterative furthest point sampling to select a set of npoint features that have the largest
minimum distance
:param ctx:
:param xyz: (B, N, 3) where N > npoint
:param npoint: int, number of features in the sampled set
:return:
output: (B, npoint) tensor containing the set
"""
assert xyz.is_contiguous()
B, N, _ = xyz.size()
output = torch.cuda.IntTensor(B, npoint)
temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
return output
def forward(ctx, points_xyz: torch.Tensor,
num_points: int) -> torch.Tensor:
"""forward.
Args:
points_xyz (Tensor): (B, N, 3) where N > num_points.
num_points (int): Number of points in the sampled set.
Returns:
Tensor: (B, num_points) indices of the sampled points.
"""
assert points_xyz.is_contiguous()
B, N, _ = points_xyz.size()
output = torch.cuda.IntTensor(B, num_points)
temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
pointnet2.furthest_point_sampling_wrapper(B, N, num_points, points_xyz,
temp, output)
ctx.mark_non_differentiable(output)
return output
