计算机与数字工程2017，Vol.45Issue(12)：2341-2345,2388,6.DOI:10.3969/j.issn.1672-9722.2017.12.004
基于主成分分析与栅格划分的点云压缩算法研究
Research on the Algorithm of Point Cloud Compression Based on Principal Component Analysis and Grid Divison
付忠敏 1张星 1孙志刚1
作者信息
- 1. 华中科技大学自动化学院 武汉 430074
- 折叠
摘要
Abstract
Three dimensional laser scanner can obtain a large number of highly dense point cloud data through non-contact measurement in a short time.Aiming at the issue that the large amount of data leads to the high resource consumption and the slow data processing speed,a point cloud compression algorithem based on principal component analysis and space grid division is intro?duced.In this algorithm,local feature descriptor of point are established via principal component analysis of the neighborhood points and the point cloud space is divided into grids,the feature points defined by the local feature descriptor in the grid are retained while non-feature points are eliminated.Experimental results show that the algorithm can compress the point cloud data while pre?serving the local details of the original model.关键词
点云压缩/主成分分析/栅格划分/局部特征/特征点Key words
point cloud compression/principal component analysis/grid division/local feature/feature point分类
信息技术与安全科学引用本文复制引用
付忠敏,张星,孙志刚..基于主成分分析与栅格划分的点云压缩算法研究[J].计算机与数字工程,2017,45(12):2341-2345,2388,6.
