Evaluating the Benefits of Octree-based Indexing for LiDAR Data

Very large true three-dimensional (3D) point datasets, as opposed to the previously common 2.5D data sets, are progressively more common nowadays, such as from Light Detection and Ranging (LiDAR). Increasingly, attempts are made to exploit these 3D point data sets beyond mere visualization. However, current Spatial Information Systems provide only limited 3D support. Even commercial systems advertising in-built, 3D data types provide only minimal functionality. Particularly, there is no effective means of indexing large 3D point datasets, which is crucial for efficient analysis and engineering usage. Also many datasets are information rich (e.g. contain color or some other associated semantic information), which has yet to be fully exploited. This paper presents the implementation in a commercial spatial database of a spatial indexing technique using an octree data structure and highlights its advantages for sparse, as well as uniformly distributed, aerial LiDAR data. The implementation outperforms an existing r-tree index within the software, and offers additional functionality of attribute-based 3D grouping