Density self-adapting non-uniform point cloud simplifying treatment method

Abstract

The invention provides a density self-adapting non-uniform point cloud simplifying treatment method. The method comprises the following steps of: 1, performing K-near neighbor calculation of the whole point cloud; 2, performing self-adaptive and uniform re-sampling according to density information and curvature information of each point; and 3, setting an initial point cloud (D), outputting a simplified point cloud (FD), and working out the average density of the point cloud, which is a weighted average of the density of all points. In the method, the average density threshold value of the point cloud is set; the initial point cloud(D) is sampled by an uniform grid curvature adaptive sampling method so as to obtain a sub-point set(SD) of the initial point cloud(D); a similarity degree matrix S is obtained by working out the degree of similarity between points in the SD, and the u value of the points in the SD by indexing; by adopting a near neighbor clustering algorithm and using the S and u as AP algorithm inputs, a representative degree matrix and an adaptive degree matrix between the points are worked out; and the information between the points are updated by iteration until the target value is reached and the final point set(FD) is obtained. The method has the advantages of simplifying calculation, reducing occupied memory capacity and effectively simplifying non-uniform point cloud.

Description

technical field The invention relates to the fields of computer vision, data processing, computer graphics, numerical calculation methods and reverse engineering, in particular to a simplified processing method for non-uniform point clouds. Background technique Large-scale sampling points or point clouds can be obtained through image matching and scanning real object model technology. Point clouds usually contain a large number of data points and can well represent the surface of objects. However, large-scale point clouds bring great difficulties to the drawing and editing of points. On the other hand, the expression of 3D models usually does not require so many points. In order to express and draw 3D point cloud models more effectively, many methods proposed in recent years have been applied to point cloud simplification. In the initial research on point clouds, most of the research was based on point-based topological grids. An overview of four classic simplification alg...

AI Technical Summary

Problems solved by technology

In order to overcome the shortcomings of the existing point cloud simplification processing methods, which are complex in calculation, need to occupy a large memory, and cannot effectively process non-uniform point clouds, the present

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