2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing

TL;DR

The paper introduces 2L-LSH, a two-step hash function method for rapid and accurate neighbor search in large-scale 3D point clouds, outperforming traditional spatial data structures.

Contribution

It proposes a novel two-step hash-based algorithm that significantly improves search speed for large point clouds compared to Kd-tree and Octree methods.

Findings

2L-LSH reduces kNN search time by over 51% compared to Kd-tree.

2L-LSH reduces RN search time by over 54% compared to Kd-tree.

The method outperforms Kd-tree and Octree in speed while maintaining accuracy.

Abstract

The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby presenting significant challenges in point cloud processing. Fast neighboring points search is one of the most common problems, which is frequently used in model reconstruction, classification, retrieval and feature visualization. Hash function is well known for its high-speed and accurate performance in searching high-dimensional data, which is also the core of the proposed 2L-LSH. Specifically, the 2L-LSH algorithm adopts a two-step hash function strategy, in which the popular step divides the bounding box of the point cloud model and the second step constructs a generalized table-based data structure. The proposed 2L-LSH offers a highly efficient and accurate solution for fast neighboring points search in large-scale 3D point cloud models, making it a promising technique for various applications in the field. The proposed algorithm is compared with the well-known methods including Kd-tree and Octree; the obtained results demonstrated that the proposed method outperforms Kd-tree and Octree in terms of speed, i.e. the time consumption of kNN search can be 51.111% and 94.159% lower than Kd-tree and Octree, respectively. And the RN search time can be 54.519% and 41.840% lower than Kd-tree and Octree, respectively.