Open AccessA Massive Laser Point Cloud Data Organization Strategy Based on the Mixed Model
Author(s) -
Yingying Wang,
Yuanjian Zhang,
Shangqiang Wang,
Xiaojia Ji
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012177
Subject(s) - octree , point cloud , computer science , grid , visualization , data mining , distributed computing , key (lock) , cloud computing , scheduling (production processes) , process (computing) , lidar , real time computing , algorithm , computer vision , remote sensing , geography , engineering , computer security , operating system , geodesy , operations management
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.