摘要
针对采集到的点云数据中含有大量的冗余数据,为后期数据处理及其应用带来诸多不便,而采用现有通用压缩方法压缩后的点云容易造成细节特征丢失问题,为此,本文提出一种基于特征点和SIFT关键点提取的点云数据压缩方法。该方法的核心技术是首先根据查询点与邻域中的点所构成向量的夹角而提取边界点;然后根据点云数据的曲率和法向量夹角提取尖锐点,据此使特征点在点云压缩处理过程中得到绝对被保留;同时在平坦区域提取SIFT关键点,这样能避免在曲率变化缓慢区域所保留的并不是特征点;最后融合特征点和SIFT关键点而实现对点云数据的压缩处理。研究通过设计与现有两种基于曲率压缩方法进行对比实验分析,结果表明本文所提方法既能最大量的去除冗余数据,又能保留点云中大部分特征点,实现了点云数据的高质量压缩。
The collected point cloud data contains a lot of redundant data,which brings a lot of inconvenience to the later data processing and its application.However,the point cloud compressed by the existing general compression method is likely to cause the loss of detailed features.Therefore,this paper proposes a point cloud data compression method based on the extraction of feature points and SIFTS key points.The core technology of this method is to first extract the boundary points according to the angle between the query point and the vector formed by the points in the neighborhood.Then extract the sharp points according to the curvature of the point cloud data and the angle between the normal vectors,so that the feature points are absolutely retained during the cloud compression process.At the same time,SIFT key points are extracted in flat areas,which can avoid the retention of feature points in areas where the curvature changes slowly.Finally,the feature points and SIFT key points are merged to realize the compression processing of the point cloud data.The study is compared with the existing two methods based on curvature compression,and the results show that the method proposed in this paper can not only remove redundant data to the greatest extent,but also retain most of the feature points in the point cloud.High quality compression of point cloud data is achieved.
作者
李绕波
袁希平
甘淑
毕瑞
胡琳
LI Rao-bo;YUAN Xi-ping;GAN Shu;BI Rui;HU Lin(Faculty of Land Resources and Engineering,Kunming University of Science and Technology,Kunming 650093,China;Yunnan Provincial Plateau Mountain Survey Technique Application Engineering Research Center,Kunming University of Science and Technology,Kunming 650093,China;College of Engineering,West Yunnan University of Applied Sciences,Dali 671009.China)
出处
《激光与红外》
CAS
CSCD
北大核心
2021年第9期1129-1136,共8页
Laser & Infrared
基金
国家自然科学基金项目(No.41861054)资助。
关键词
点云压缩
特征点
边界点
尖锐点
SIFT关键点
point cloud compression
feature point
boundary point
sharp point
SIFT key point
作者简介
李绕波(1994-),男,主要从事地面三维激光扫描理论与技术应用研究;通讯作者:甘淑(1964-),女,教授,主要从事资源环境遥感与GIS空间分析技术应用。E-mail:1193887560@qq.com。
