A Hybrid Spatial Index for Massive Point Cloud Data Management and Visualization

With the wide use of laser scanning technology, point cloud data collected from airborne sensors and terrestrial sensors are often integrated to depict a complete scenario from the top and ground views, even though points from different platforms and sensors have quite different densities. These massive point clouds with various structures create many problems for both data management and visualization. In this article, a hybrid spatial index method is proposed and implemented to manage and visualize integrated point cloud data from airborne and terrestrial scanners. This hybrid spatial index structure combines an extended quad‐tree model at the global level to manage large area airborne sensor data, with a 3‐D R‐tree to organize high density local area terrestrial point clouds. These massive point clouds from different platforms have diverse densities, but this hybrid spatial index system has the capability to organize the data adaptively and query efficiently, satisfying the requirements for fast visualization. Experiments using point cloud data collected from the Dunhuang area were conducted to evaluate the efficiency of our proposed method.