open3d.ml.torch.ops.fixed_radius_search¶

open3d.ml.torch.ops.
fixed_radius_search
(points, queries, radius, points_row_splits, queries_row_splits, hash_table_splits, hash_table_index, hash_table_cell_splits, index_dtype=3, metric='L2', ignore_query_point=False, return_distances=False)¶ Computes the indices of all neighbors within a radius.
This op computes the neighborhood for each query point and returns the indices of the neighbors and optionally also the distances. The same fixed radius is used for each query point. 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], [0.5,2.1,2.1], ] radius = 1.0 # build the spatial hash table for fixex_radius_search table = ml3d.ops.build_spatial_hash_table(points, radius, points_row_splits=torch.LongTensor([0,5]), hash_table_size_factor=1/32) # now run the fixed radius search ml3d.ops.fixed_radius_search(points, queries, radius, points_row_splits=torch.LongTensor([0,5]), queries_row_splits=torch.LongTensor([0,3]), **table._asdict()) # 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], ]) radius = 1.0 # build the spatial hash table for fixex_radius_search table = ml3d.ops.build_spatial_hash_table(points, radius, points_row_splits=torch.LongTensor([0,5]), hash_table_size_factor=1/32) # now run the fixed radius search ml3d.ops.fixed_radius_search(points, queries, radius, points_row_splits=torch.LongTensor([0,5]), queries_row_splits=torch.LongTensor([0,3]), **table._asdict()) # returns neighbors_index = [1, 4, 4] # neighbors_row_splits = [0, 1, 2, 3] # neighbors_distance = []
 index_dtype:
The data type for the returned neighbor_index Tensor. Either int32 or int64. Default is int32.
 metric:
Either L1, L2 or Linf. 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 tensor ‘neighbors_distance’. If False a zero length Tensor will be returned for ‘neighbors_distance’.
 points:
The 3D positions of the input points.
 queries:
The 3D positions of the query points.
 radius:
A scalar with the neighborhood radius
 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.
 hash_table_splits: Array defining the start and end the hash table
for each batch item. This is [0, number of cells] if there is only 1 batch item or [0, hash_table_cell_splits_size1] which is the same.
 hash_table_index: Stores the values of the hash table, which are the indices of
the points. The start and end of each cell is defined by hash_table_cell_splits.
hash_table_cell_splits: Defines the start and end of each hash table cell.
 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. Note that the distances are squared if metric is L2. This is a zero length Tensor if ‘return_distances’ is False.