期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B5
页码：477-484
出版社：Copernicus Publications
摘要：Principal Component Analysis (PCA) is often utilised in point cloud processing as provides an efficient method to approximate local point properties through the examination of the local neighbourhoods. This process does sometimes suffer from the assumption that the neighbourhood contains only a single surface, when it may contain multiple discrete surface entities, as well as relating the properties from PCA to real world attributes. This paper will present two methods. The first is a correction method to filter out the presence of multiple surfaces through an iterative process. The second is to combine the PCA preformed on the neighbourhood of point coordinates and normal approximations in order to estimate the radius of curvature in the maximum and minimum curvature directions
关键词：Laser Scanning; Point clouds; Curvature Approximation; Surface Normal Estimation; Principal Component ; Analysis
