Cheesemap: A High-Performance Point-Indexing Data Structure for Neighbor Search in LiDAR Data

TL;DR

Cheesemap is a new high-performance data structure designed for efficient neighbor search in 3D LiDAR point clouds, outperforming existing methods in speed and memory efficiency, especially for aerial laser scanning data.

Contribution

The paper introduces cheesemap, a novel point-indexing data structure optimized for LiDAR data, with three variants to handle different point cloud sparsity levels, improving search speed and memory use.

Findings

Cheesemap outperforms state-of-the-art data structures in query execution time.

Sparse and mixed cheesemap variants use less memory.

Cheesemap is particularly effective for aerial laser scanning point clouds.

Abstract

Point cloud data, as the representation of three-dimensional spatial information, is a fundamental piece of information in various domains where indexing and querying these point clouds efficiently is crucial for tasks such as object recognition, autonomous navigation, and environmental modeling. In this paper, we present a comprehensive comparative analysis of various data structures combined with neighboring search methods across different types of point clouds. Additionally, we introduce a novel data structure, cheesemap, to handle 3D LiDAR point clouds. Exploring the sparsity and irregularity in the distribution of points, there are three flavors of the cheesemap: dense, sparse, and mixed. Results show that the cheesemap can outperform state-of-the-art data structures in terms of execution time per query, particularly for ALS (Aerial Laser Scanning) point clouds. Memory consumption is also minimal, especially in the sparse and mixed representations, making the cheesemap a suitable choice for applications involving three-dimensional point clouds.