fastdev.geom.ball_query

Module Contents

fastdev.geom.ball_query.ball_query(p1: torch.Tensor, p2: torch.Tensor, lengths1: torch.Tensor | None = None, lengths2: torch.Tensor | None = None, K: int = 500, radius: float = 0.2, return_nn: bool = True)[source]

Ball Query is an alternative to KNN. It can be used to find all points in p2 that are within a specified radius to the query point in p1 (with an upper limit of K neighbors).

The neighbors returned are not necssarily the nearest to the point in p1, just the first K values in p2 which are within the specified radius.

This method is faster than kNN when there are large numbers of points in p2 and the ordering of neighbors is not important compared to the distance being within the radius threshold.

“Ball query’s local neighborhood guarantees a fixed region scale thus making local region features more generalizable across space, which is preferred for tasks requiring local pattern recognition (e.g. semantic point labeling)” [1].

[1] Charles R. Qi et al, “PointNet++: Deep Hierarchical Feature Learning

on Point Sets in a Metric Space”, NeurIPS 2017.

Parameters:

  • p1 (torch.Tensor) – Tensor of shape (N, P1, D) giving a batch of N point clouds, each containing up to P1 points of dimension D. These represent the centers of the ball queries.

  • p2 (torch.Tensor) – Tensor of shape (N, P2, D) giving a batch of N point clouds, each containing up to P2 points of dimension D.

  • lengths1 (Union[torch.Tensor, None]) – LongTensor of shape (N,) of values in the range [0, P1], giving the length of each pointcloud in p1. Or None to indicate that every cloud has length P1.

  • lengths2 (Union[torch.Tensor, None]) – LongTensor of shape (N,) of values in the range [0, P2], giving the length of each pointcloud in p2. Or None to indicate that every cloud has length P2.

  • K (int) – Integer giving the upper bound on the number of samples to take within the radius

  • radius (float) – the radius around each point within which the neighbors need to be located

  • return_nn (bool) – If set to True returns the K neighbor points in p2 for each point in p1.

Returns:

Tensor of shape (N, P1, K) giving the squared distances to

the neighbors. This is padded with zeros both where a cloud in p2 has fewer than S points and where a cloud in p1 has fewer than P1 points and also if there are fewer than K points which satisfy the radius threshold.

idx: LongTensor of shape (N, P1, K) giving the indices of the

S neighbors in p2 for points in p1. Concretely, if p1_idx[n, i, k] = j then p2[n, j] is the k-th neighbor to p1[n, i] in p2[n]. This is padded with -1 both where a cloud in p2 has fewer than S points and where a cloud in p1 has fewer than P1 points and also if there are fewer than K points which satisfy the radius threshold.

nn: Tensor of shape (N, P1, K, D) giving the K neighbors in p2 for

each point in p1. Concretely, p2_nn[n, i, k] gives the k-th neighbor for p1[n, i]. Returned if return_nn is True. The output is a tensor of shape (N, P1, K, U).

Return type:

dists