文献类型：期刊文献

中文题名：最小二乘支持向量机的点云数据孔洞修补算法

英文题名：Hole Repairing Algorithm for Point Cloud Data Based on Least Square Support Vector Machine

作者：杨永强[1];李淑红[1]

第一作者：杨永强

机构：[1]河南财经政法大学计算机与信息工程学院

第一机构：河南财经政法大学计算机与信息工程学院

年份：2018

卷号：56

期号：3

起止页码：692-696

中文期刊名：吉林大学学报：理学版

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD2017_2018】；

基金：国家自然科学基金(批准号:61202285)

语种：中文

中文关键词：三维成像;曲面重建;点云数据;孔洞修补;最小二乘支持向量机

外文关键词：three dimensional imaging;surface reconstruction;point cloud data;hole repairing;least square support vector machine （LSSVM）

摘要：为了获得理想的点云数据孔洞修补结果,针对当前算法存在的缺陷,提出一种基于最小二乘支持向量机(LSSVM)的点云数据孔洞修补算法.首先根据散乱点云边界估计孔洞修补范围,然后根据孔洞及周围点的信息,采用最小二乘支持向量机建立一个曲面,并对曲面点云数据的孔洞进行修补,最后采用C++语言编程实现仿真实验.实验结果表明,最小二乘支持向量机能有效修补各种复杂的孔洞,且修补效果优于其他算法.
In order to obtain the ideal hole repairing result of point cloud data,aiming at the defects existing in the current algorithms,we proposed a hole repairing algorithm for point cloud data based on least square support vector machine.First,the hole repairing range was estimated according to the boundary of scattered point cloud,and then according to information of hole and surrounding points,we built a surface by least square support vector machine,and repaired the hole in the point cloud data.Finally,the simulation experiment was realized by C＋＋ language programming. The experimental results show that the least square support vector machine can effectively repair various complex holes,and the repair effect is better than other algorithms.