A massive point cloud spatial management method based on octree-like encoding

Abstract

A mass point cloud spatial management method based on octree-like encoding takes the massive point cloud data as the research object, and is a hybrid point cloud index structure which combines the spatial segmentation of KD-tree to achieve efficient management of mass point cloud. The method is characterized in that firstly, the space of point cloud is partitioned by KD tree, and the subspace is further partitioned by octree, and the octree-like index structure is established; the traditional linear octree coding is improved and the optimized coding method is used to encode the space in orderto achieve better spatial management and neighborhood search. The experimental results and comparative analysis show that octree-like structure can make the overall structure of data organization morereasonable, improve the access efficiency and reduce the occupancy of memory space. The method not only improves the speed of traditional KD tree index, but also improves the problem that traditionaloctree occupies too much space and takes too long to search neighborhood, and realizes the reasonable management of massive point cloud space.

Description

technical field [0001] The invention relates to the field of three-dimensional information model data processing, in particular to a massive point cloud space management method based on octree-like coding. Background technique [0002] With the advancement of computer technology and the increasing demand for digitization, 3D models, as a new type of multimedia digital technology, are developing at a rapid pace. In recent years, 3D laser scanning technology has developed rapidly. With the continuous improvement of its performance in terms of efficiency, accuracy, ranging range, etc. and the development of related theories, 3D scanning technology has been applied in more and more fields. Three-dimensional laser scanning directly performs three-dimensional dense sampling on the earth's surface, and can quickly obtain massive and irregular spatially distributed three-dimensional point clouds with three-dimensional coordinates (X, Y, Z) and certain attributes (reflection intensit...

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Problems solved by technology

[0026] However, among the various three-dimensional space combination indexing methods, there is no one method that is obviously superior to other methods at present. Each method has its own advantages and disadvantages. How to choose various indexing methods to complement each other needs to be further explored.

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