Point-cloud feature point extraction method based on local sub-neighborhood division

Abstract

The invention provides a point-cloud feature point extraction method based on local sub-neighborhood division. The method includes the four steps of the initial feature point extraction stage, the local triangle construction stage, the local sub-neighborhood division stage and the robust feature point extraction stage. At the initial feature point extraction stage, potential initial feature points of input point cloud data are acquired. At the local triangle construction stage, in order to effectively extract true feature points from an initial feature point set, a triangle set capable of reflecting the local geometrical feature structure of the corresponding point is established in a local neighborhood of each initial feature point. At the local sub-neighborhood division stage, the normal of each constructed local triangle set is clustered, local neighborhood points at one point can be clustered, and thus sub-neighborhood division of the local neighborhood points at one point can be achieved. At the robust feature point extraction stage, local plane fitting is conducted on the data points in the divided sub-neighborhood, and accordingly the true feature points can be recognized by judging whether current points fall on intersection lines of a plurality of planes at the same time or not.

Description

technical field [0001] The invention relates to a point cloud feature point extraction method based on local sub-neighborhood division. Background technique [0002] With the rapid development of 3D scanning acquisition technology, point cloud data processing research has become a research hotspot in the development of digital geometry processing research, and has been widely concerned and applied in the fields of industrial design, art, cultural relics restoration and protection. With the deepening of the research, the research on the robust feature extraction of point cloud data has profound theoretical significance and broad application prospects. [0003] Features are an important part of the geometric model, and play an important role in the appearance of the geometric model and the accurate expression of the geometric model. In recent years, the feature extraction of mesh models has been extensively studied by scholars at home and abroad, and has been successfully app...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is: to overcome the deficiency that the existing point cloud feature point extraction algorithm has a great influence on noise interference, and to provide a feature point extraction method based on local sub-neighborhood division

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