Massive Point Cloud Space Management Method Based on Octree-Like Encoding

Abstract

Based on the mass point cloud data, this paper proposes a hybrid octree mixing point cloud index structure which combines the KD-tree spatial segmentation idea to realize the efficient management of mass point cloud. In this paper, the space of the point cloud is firstly divided by the KD-tree idea. On this basis, the octree is used for further segmentation to establish an octree-like index structure. Then the point cloud dataset is spatially encoded using the improved encoding to achieve better spatial management and neighborhood search. Finally, using five groups of incremented point cloud set as test data, the experimental results and comparison analysis show that the octree-like space can make the overall structure of the data organization more reasonable, effectively improve the access efficiency and reduce the occupancy of memory space. The index structure not only improves the speed of the traditional KD-tree construction index but also improves the problem that the traditional octree is too large for space occupation and the neighborhood search takes too long. It achieves reasonable management of massive point cloud space.