A Massive Laser Point Cloud Data Organization Strategy Based on the Mixed Model

In the process of data interaction, neighborhood query, filtering, visualization, and dynamic update of LiDAR point cloud data, how to efficiently organize and process massive point cloud data, and quickly index and locate any point in the point cloud and its neighborhood Search is a key issue to be solved urgently. In this paper, combining the advantages of a virtual grid with no interpolation loss on original data, sT spatial relationship, and low memory occupation of the octree, we design an index method based on the combination of virtual grid and adaptive octree based on dynamic scheduling of internal and external memory. Realize the organization and scheduling of massive LiDAR laser scanning point cloud data. (paper)