open3d.ml.torch.ops.radius_search¶

open3d.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 open3d.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 open3d.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.