cloudViewer.ml.torch.ops.radius_search

cloudViewer.ml.torch.ops.radius_search(points, queries, radii, points_row_splits, queries_row_splits, index_dtype=3, metric='L2', ignore_query_point=False, return_distances=False, normalize_distances=False)

Computes the indices and distances of all neighbours within a radius.

This op computes the neighborhood for each query point and returns the indices of the neighbors and optionally also the distances. Each query point has an individual search radius. Points and queries can be batched with each batch item having an individual number of points and queries. The following example shows a simple search with just a single batch item:

import cloudViewer.ml.tf as ml3d

points = [
    [0.1,0.1,0.1],
    [0.5,0.5,0.5],
    [1.7,1.7,1.7],
    [1.8,1.8,1.8],
    [0.3,2.4,1.4]]

queries = [
    [1.0,1.0,1.0],
    [0.5,2.0,2.0],
    [0.5,2.1,2.2],
]

radii = [1.0,1.0,1.0]

ml3d.ops.radius_search(points, queries, radii,
                       points_row_splits=[0,5],
                       queries_row_splits=[0,3])
# returns neighbors_index      = [1, 4, 4]
#         neighbors_row_splits = [0, 1, 2, 3]
#         neighbors_distance   = []


# or with pytorch
import torch
import cloudViewer.ml.torch as ml3d

points = torch.Tensor([
  [0.1,0.1,0.1],
  [0.5,0.5,0.5],
  [1.7,1.7,1.7],
  [1.8,1.8,1.8],
  [0.3,2.4,1.4]])

queries = torch.Tensor([
    [1.0,1.0,1.0],
    [0.5,2.0,2.0],
    [0.5,2.1,2.1],
])

radii = torch.Tensor([1.0,1.0,1.0])

ml3d.ops.radius_search(points, queries, radii,
                       points_row_splits=torch.LongTensor([0,5]),
                       queries_row_splits=torch.LongTensor([0,3]))
# returns neighbors_index      = [1, 4, 4]
#         neighbors_row_splits = [0, 1, 2, 3]
#         neighbors_distance   = []

metric: Either L1 or L2. Default is L2

ignore_query_point: If true the points that coincide with the center of the

search window will be ignored. This excludes the query point if queries and points are the same point cloud.

return_distances: If True the distances for each neighbor will be returned in

the output tensor neighbors_distance. If False a zero length Tensor will be returned for neighbors_distances.

normalize_distances: If True the returned distances will be normalized with the

radii.

points: The 3D positions of the input points.

queries: The 3D positions of the query points.

radii: A vector with the individual radii for each query point.

points_row_splits: 1D vector with the row splits information if points is

batched. This vector is [0, num_points] if there is only 1 batch item.

queries_row_splits: 1D vector with the row splits information if queries is

batched. This vector is [0, num_queries] if there is only 1 batch item.

neighbors_index: The compact list of indices of the neighbors. The

corresponding query point can be inferred from the neighbor_count_row_splits vector.

neighbors_row_splits: The exclusive prefix sum of the neighbor count for the

query points including the total neighbor count as the last element. The size of this array is the number of queries + 1.

neighbors_distance: Stores the distance to each neighbor if return_distances

is True. The distances are squared only if metric is L2. This is a zero length Tensor if return_distances is False.