def nearest_neighbor_interpolate(unknown, known, known_feats):
"""
:param pts: (n, 4) tensor of the bxyz positions of the unknown features
:param ctr: (m, 4) tensor of the bxyz positions of the known features
:param ctr_feats: (m, C) tensor of features to be propigated
:return:
new_features: (n, C) tensor of the features of the unknown features
"""
dist, idx = pointnet2_utils.three_nn(unknown, known)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=1, keepdim=True)
weight = dist_recip / norm
interpolated_feats = pointnet2_utils.three_interpolate(
known_feats, idx, weight)
return interpolated_feats
def nearest_neighbor_interpolate(unknown, known, known_feats):
"""
:param pts: (n, 4) tensor of the bxyz positions of the unknown features [34114, 4]
:param ctr: (m, 4) tensor of the bxyz positions of the known features [52913, 4]
:param ctr_feats: (m, C) tensor of features to be propigated [52913, 32]
:return:
new_features: (n, C) tensor of the features of the unknown features
"""
dist, idx = pointnet2_utils.three_nn(
unknown, known) # dist: [34114, 3], idx: [34114, 3]
dist_recip = 1.0 / (dist + 1e-8) # [34114, 3]
norm = torch.sum(dist_recip, dim=1, keepdim=True) # [34114, 1]
weight = dist_recip / norm # 权重值 [34114, 3]
interpolated_feats = pointnet2_utils.three_interpolate(
known_feats, idx, weight) # [34114, 3]
return interpolated_feats