Method for finding topological neighbors in sampled data of physical surface

Abstract

The invention provides a method for finding topological neighbors in sampled data of physical surface and belongs to the technical field of reverse product engineering. The method is characterized in that constructing a dynamic spatial index for physical surface sample points through an R* tree; subjecting the index to depth-first traversal to quickly acquire a k-nearest neighbor point set of target sample points, using the k-nearest neighbor point set as initial reference data for topological neighbors of the target sample points, describing distribution law of the sample points by kernel density estimation according to a relation between neighbor finding and sample point distribution, ranking the sample points in the initial reference data according to their probability densities, selecting Omega sample points of highest probability density to define a maximum point of local probability densities, determining a finding direction according to the maximum point, allowing the initial reference data to extend moderately to a sparse area so as to reduce the degree of neighbor information loss, and performing iterative computing so as to acquire complete topological neighbor data of the target sample points. The method has the advantages that topological neighbor data of uniform or non-uniform sampled data of a complex profile can be acquired fast, finding results include k neighbors, Voronoi neighbors and other effective neighbor data, and the local profile features of the sampled data of can be better indicated.

Description

technical field [0001] The invention provides a method for querying topological neighborhoods of sampled data on the surface of a physical object, belonging to the technical field of product reverse engineering. Background technique [0002] In reverse engineering, the surface feature analysis technology of sampled data on the physical surface is widely used to analyze the characteristic area of ​​the surface information expressed by the sampled data, and the analysis results are used as the characteristic reference data for surface modeling. The accuracy of surface feature analysis results has an important impact, and its query speed directly determines the efficiency of surface feature analysis. [0003] At present, the commonly used neighborhood data query methods are k Neighborhood query, Delaunay neighborhood query, Voronoi neighborhood query, etc. k Neighborhood query is currently the most widely used neighborhood query method, based on Euclidean distance query to ob...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: to overcome the problems of low adaptability to non-uniform sampling data and low query efficiency in existing neighborhood query methods, and to provide a topological neighborhood query method for surface sampling data of real objects, which is fast, Accurately query and obtain topological neighborhood data of surface sampling data of arbitrary complex objects

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