一种改进的点云数据组合精简算法  

Improved algorithm for point cloud data simplification

作　　者：尹星翔 唐平[1] 黄永慧[1] 钟灵[1] 

机构地区：[1]广东工业大学自动化学院,广东广州510006

出　　处：《无线互联科技》2016年第13期94-97,共4页Wireless Internet Technology

基　　金：广东省科技计划项目;项目编号:2013b010404019;广州市花都区科技计划项目;项目编号:HD13CXY-009

摘　　要：在逆向工程中,点云数据的精简是一个重要的步骤,精简的质量直接关系到后续曲面重构的效率。文章针对广州灰塑曲率较大,凹凸面较多的特点,提出了一种基于曲率和均匀精简的点云数据精简方法:利用包围盒法对散乱的点云数据进行拓扑规则排序,建立点的K-邻域集,计算点云在某点处的曲率,在曲率较大处保留更多的特征点,但是曲率较小处会删除较多的点云数据。文章在此基础上再利用均匀网格的方法对初始精简后的点云进行重采样处理,使得曲率较小处的特征点也能保留下来。Point cloud reduction is an important step of reverse engineering.The quality of reduction is directly related to the efifciency of subsequent surface reconstruction.This paper ifrst analysed the clay sculpture of Guangzhou which is characterized by large curvature and concave and convex surface, and proposed point cloud reduction method.This method uses bounding box method topological order scattered-point cloud data, sets up K-nearest Neighbors, and calculates the curvature of the point cloud at some point where we keep more point with larger curvature and delete more point cloud with smaller curvature.Then it uses uniform grid for resampling process to retain some feature points.

关 键 词：逆向工程 灰塑 曲率 均匀精简 点云数据 K-邻域 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]