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

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.