Three-dimensional point cloud data simplification algorithm based on ray theory

Abstract

The invention relates to a three-dimensional point cloud data simplification algorithm based on the ray theory. According to the algorithm, it is assumed that rays are uniformly produced in all directions from the center point of a three-dimensional point cloud model and fill the whole three-dimensional space. For the point cloud model in the space, if the distance between a point in the model and the ray nearest to the point is smaller than a given threshold value, the point is viewed as a point needing simplification, and the result can be obtained easily. The more intensive the rays, the larger the threshold value, and the easier the simplification of data points in the three-dimensional point cloud model. Therefore, simplification effects of different degrees can be achieved by controlling the number of the rays and other threshold values. The invention provides a three-dimensional point cloud data simplification algorithm not based on curvature calculation. The algorithm has a good simplification effect and high efficiency within a certain range of simplification degrees.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a three-dimensional point cloud data reduction algorithm based on the ray principle. Background technique [0002] In recent years, with the reduction of cost and the improvement of precision of 3D scanners, 3D point cloud data has become an important form of data representation in the fields of graphics, reverse engineering and industry. However, the amount of originally collected 3D point cloud data is very large, which brings great difficulties to the later 3D reconstruction. Therefore, denoising and simplification of 3D point cloud is a crucial link in point cloud processing. [0003] At present, the algorithms for 3D point cloud data reduction include cluster-based simplification and curvature-based simplification. Recursive operations or curvature estimation are required, so for models with a large amount of point cloud data, such algorithms are very time-consuming. [0004...

AI Technical Summary

Problems solved by technology

However, the amount of 3D point cloud data collected originally is very large, which brings great difficulties to the later 3D reconstruction. Therefore, the denoising and simplification of 3D point cloud is a crucial link in point cloud processing.

[0003] At present, the algorithms for 3D point cloud data reduction include cluster-based simplification and curvature-based simplification. Recursive operations or curvature estimation are required, so for models with a large amount of point cloud data, such algorithms are very time-consuming

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