关键词: 法向估计, 曲面局部平坦度, 贝叶斯信息准则, 聚类分析

Abstract: To improve the accuracy of estimating sample of complex surfaces with the features such as edges and sharp corners,an estimation method for normal point clouds based on clustering classification of characteristic areas was proposed.Point clouds were hierarchically segmented into smoothness,feature boundaries,edges,sharp corners and other areas and feature types of sample points were identified by clustering analysis based on the local smoothness of surfaces and Bayesian Information Criterion.The results of normal estimation of smooth areas iteratively spread to neighbouring characteristic areas to make the normal feature points be consistent with the neighbouring smooth areas.The experiments results showed that the method could accurately estimate the normal of characteristic areas' sample points,effectively ensure the normal polysemy to the sample points of edges and sharp corners areas and suppress data noise.

Key words: normal estimation, local smoothness of surfaces, Bayesian information criterion, cluster analysis