def sample_and_group(npoint, nsample, xyz, points):
B, N, C = xyz.shape
S = npoint
fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint]
new_xyz = index_points(xyz, fps_idx)
new_points = index_points(points, fps_idx)
dists = square_distance(new_xyz, xyz) # B x npoint x N
idx = dists.argsort()[:, :, :nsample] # B x npoint x K
grouped_points = index_points(points, idx)
grouped_points_norm = grouped_points - new_points.view(B, S, 1, -1)
new_points = torch.cat([
grouped_points_norm,
new_points.view(B, S, 1, -1).repeat(1, 1, nsample, 1)
],
dim=-1)
return new_xyz, new_points
def _get_item(self, index):
if index in self.cache:
point_set, cls = self.cache[index]
else:
fn = self.datapath[index]
cls = self.classes[self.datapath[index][0]]
cls = np.array([cls]).astype(np.int32)
point_set = np.loadtxt(fn[1], delimiter=',').astype(np.float32)
if self.uniform:
point_set = farthest_point_sample(point_set, self.npoints)
else:
point_set = point_set[0:self.npoints,:]
point_set[:, 0:3] = pc_normalize(point_set[:, 0:3])
if not self.normal_channel:
point_set = point_set[:, 0:3]
if len(self.cache) < self.cache_size:
self.cache[index] = (point_set, cls)
return point_set, cls