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

2026-04-23

AI Summary

This study addresses the inefficiency of proximity search in large-scale 3D point clouds by proposing a two-stage locality-sensitive hashing (LSH) indexing method. The approach integrates spatial bounding volume partitioning with a generalized hash table structure to enable efficient and exact k-nearest neighbor (kNN) and radius neighborhood (RN) queries. As the first work to introduce a two-stage LSH strategy for this task, it significantly enhances both search speed and scalability. Experimental results demonstrate that, compared to Kd-tree and Octree baselines, the proposed method reduces kNN query time by 51.1% and 94.2%, respectively, and decreases RN query time by 54.5% and 41.8%.

Technology Category

• Search and Optimization: Distributed Search
• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

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.

Problem

• point cloud
• nearest neighbor search
• large-scale
• 3D data
• indexing

Innovation

• Locality-Sensitive Hashing
• Point Cloud Indexing
• Two-Level Hashing
• Nearest Neighbor Search
• 3D Data Structures