def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor: r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor where N > npoint npoint : int32 number of features in the sampled set Returns ------- torch.Tensor (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""" return _ext.furthest_point_sampling(xyz, npoint)
def forward(ctx, xyz, npoint): # type: (Any, torch.Tensor, int) -> torch.Tensor r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor where N > npoint npoint : int32 number of features in the sampled set Returns ------- torch.Tensor (B, npoint) tensor containing the set """ return _ext.furthest_point_sampling(xyz, npoint)
def forward(ctx, xyz, npoint): # type: (Any, torch.Tensor, int) -> torch.Tensor r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor where N > npoint npoint : int32 number of features in the sampled set Returns ------- torch.Tensor (B, npoint) tensor containing the set """ xyz = xyz.contiguous() out = _ext.furthest_point_sampling(xyz, npoint) ctx.mark_non_differentiable(out) return out.type(torch.long).cuda()