基于点重要性判断的点云简化

为了有效保持散乱点云的显著几何特征,提高点云简化的精度和效率,提出一种点重要性判断点云简化方法.首先,计算点云中点的重要性,并根据重要性提取特征点;然后,采用八叉树算法对非特征点进行简化,从而保留点云的主要细节特征,实现点云简化处理;最后,通过对公共点云和文物点云数据模型的简化实验来验证该点云简化方法.结果表明,该点重要性判断点云简化方法可以在有效保持点云细节几何特征的同时,实现点云的有效简化,是一种快速、高精度的点云简化方法.

Point Cloud Simplification Based on Point Importance Judgment

In order to effectively maintain the significant geometric features of scattered point clouds and improve the accuracy and efficiency of point cloud simplification, a point cloud simplification method based on point importance judgment is proposed. Firstly, the importance of points in point clouds is calculated, and feature points are extracted according to the importance. Then, the octree algorithm is used to simplify the non-feature points, so as to retain the main details of the point cloud and realize the simplification of the point cloud. Finally, the point cloud simplification method is verified by simplifying the data model of public point clouds and cultural relics point clouds. The results show that the point cloud simplification method based on point importance judgment can effectively simplify the point cloud while maintaining the detailed geometric characteristics of the point cloud. It is a fast and high-precision point cloud simplification method.