in point_e/evals/pointnet2_utils.py [0:0]
def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False, deterministic=False):
"""
Input:
npoint:
radius:
nsample:
xyz: input points position data, [B, N, 3]
points: input points data, [B, N, D]
Return:
new_xyz: sampled points position data, [B, npoint, nsample, 3]
new_points: sampled points data, [B, npoint, nsample, 3+D]
"""
B, N, C = xyz.shape
S = npoint
fps_idx = farthest_point_sample(xyz, npoint, deterministic=deterministic) # [B, npoint, C]
new_xyz = index_points(xyz, fps_idx)
idx = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]
grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)
if points is not None:
grouped_points = index_points(points, idx)
new_points = torch.cat(
[grouped_xyz_norm, grouped_points], dim=-1
) # [B, npoint, nsample, C+D]
else:
new_points = grouped_xyz_norm
if returnfps:
return new_xyz, new_points, grouped_xyz, fps_idx
else:
return new_xyz, new_points