Research on the Algorithm of Point Cloud Compression Based on Principal Component Analysis and Grid Divison
FU Zhongmin
ZHANG Xing
SUN Zhigang
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.
Keywords：point cloud compressionprincipal component analysisgrid divisionlocal featurefeature point
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2341-2345,2388 )