Three-dimensional dispersion point cloud topological neighbor data query method

Abstract

The invention provides a three-dimensional scattered point cloud topological near neighbor data query method which is characterized in that: three-dimensional scattered point cloud data is collected, a dynamic hollow ball expansion algorithm is adopted to query a k-near neighbor point set based on a dynamic spatial index structure of R<*>-tree tissue three-dimensional scattered point clouds, sample point topological near neighbor reference data is obtained by the eccentric expansion and the self-adaptive expansion, a Voronoi diagram of the sample point topological near neighbor reference data is generated, data points which are corresponding to a sample point Voronoi neighborhood are queried, and the data points are the topological near neighbor data of the sample points. The use of the method can rapidly and accurately obtain the topological near neighbor data of any complicated mass scattered point clouds.

Description

Technical field

[0001] The invention provides a method for querying three-dimensional scattered point cloud topological neighbor data, which belongs to the field of computational geometry. Background technique

[0002] 3D scattered point cloud nearest neighbor data query is widely used in the fields of surface reconstruction and interpolation, geographic information system and differential geometry in reverse engineering. Its query efficiency and accuracy directly affect the speed and quality of data processing.

[0003] At present, the commonly used methods for querying 3D scattered point cloud nearest neighbor data mainly include k nearest neighbor query and local Delaunay nearest neighbor query. Among them, k-nearest neighbor query mainly adopts spatial partitioning strategy. Xiong Bangshu et al. published the academic paper "k nearest neighbors of three-dimensional scattered data in the academic journal "Journal of Computer Aided Design and Graphics" 2004, 16(7), P909-912 In ...

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