A complex curved surface part point cloud reduction method capable of effectively keeping boundary and local features

Abstract

The invention belongs to the technical field of precision machining and measurement, and discloses a complex curved surface part point cloud reduction method capable of effectively keeping boundary and local features. The method comprises the following steps: generating a scanned point cloud of a complex curved surface part; obtaining a plurality of neighborhood points for each point in the point cloud and calculating each normal line vector; finding out m points within the shortest radius range with each point being as the centre of sphere, and then, calculating the average value of included angles between the normal line vectors of the points in the point cloud and the normal line vectors of the m points; setting a threshold value based on the average value of included angles, and then, carrying out feature coarse classification; carrying out secondary subdivision to finish selection of a first reduction subset, and then, calculating directional Hausdorff distance to finish selection of a second reduction subset; and finally, merging the two reduction subsets to obtain a reduced scanned point cloud. Compared with the prior art, the method can achieve higher precision and efficiency, and can effectively keep the boundary and local features of the point cloud model.

Description

technical field [0001] The invention belongs to the technical field of precision machining and measurement, and more specifically relates to a point cloud simplification method for complex curved surface parts that can effectively preserve boundaries and local features. Background technique [0002] With the continuous development of computer vision, pattern recognition and other fields, non-contact scanning technology plays an increasingly important role in the processing and inspection of precision parts, especially in the three-dimensional modeling of workpieces, tool positioning, and geometric contours. It has a wide range of applications in detection. Based on optical principles, non-contact scanning equipment can obtain tens of thousands of three-dimensional point data within a few seconds, and for some complex structural surfaces and large-sized objects, the obtained point cloud data is very large, so it is difficult to These data are used directly for computational ...

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Problems solved by technology

[0004] In addition, further searches found that CN104881498A announced a method for Out-of-Core fast and uniform simplification of massive point clouds, which can be used to simplify the sampling data of the physical surface that exceeds the main memory tolerance, but the essence of this method is to use the bounding box to simplify Point cloud data will lose the geometric features of some point clouds during the simplification process; CN102800114A discloses a data point cloud simplification method based on Poisson-disk sampling, which can prevent Sampling points are locally gathered, but it is difficult to define sparse and dense areas in practical applications; CN104732581A discloses a point cloud simplification method based on point feature histogram, which first calculates the standard of feature histogram of each point difference, and compared with the preset standard deviation threshold, the purpose of simplification is achieved by deleting points larger than the standard deviation threshold, but because the geometric structure of the point cloud is very different, it is difficult to determine a general standard deviation threshold It is proposed to adapt the selection standard threshold through a corresponding relationship table, but the establishment process of the table is complicated and time-consuming; CN104915986A discloses an automatic modeling method for a solid three-dimensional model, which uses edge folding for the established three-dimensional grid model method, the points and edges on the three-dimensional grid model are deleted in proportion, so as to establish the three-dimensional simplified surface model of the object, because the local features and boundary points are not considered, there is a large error between the simplified model obtained by the invention and the real model

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