Point cloud simplification processing method based on resampling method and affine clustering algorithm

Abstract

The invention relates to a point cloud simplification processing method based on a resampling method and an affine clustering algorithm, which comprises the following steps: 1, setting a threshold value of the simplified target point number; 2, uniformly sampling an initial point cloud D to acquire a point subset SD of the initial point cloud D and searching a k nearest neighboring point of each point in the subset SD; 3, calculating the curvature CV of each point in the SD by using the k nearest neighboring point acquired in the step 2; 4, calculating the similarity between the points in theSD to acquire a similarity matrix S; 5, inputting S and CV as an AP algorithm by applying an AP clustering algorithm and calculating a representative degree matrix and an adaptive selecting degree matrix between the points; selecting representing points according to an iteration result; if the number D of the representing points is smaller than the threshold value, namely D=D-SD, turning to the step 2; and adding a representing point label selected each time into the same matrix till a target value is achieved to acquire a final point set FD. The invention simplifies the calculation, reducesthe occupied memory capacity and has favorable effectiveness.

Description

technical field [0001] The invention relates to the fields of computer vision, data processing, computer graphics, numerical calculation methods and reverse engineering, in particular to a point cloud simplified processing method. Background technique [0002] 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 algo...

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Problems solved by technology

[0004] 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 have poor effectiveness, the present invention provides a resampling-based method that simplifies calculation, reduces occupied memory capacity, and has good effectiveness. Point cloud simplification processing method with affine clustering algorithm

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