光学精密工程2017，Vol.25Issue(12)：3169-3178,10.DOI:10.3788/OPE.20172512.3169
保持特征的散乱点云数据去噪
Feature-preserving scattered point cloud denoising
摘要
Abstract
To move the outliers and noisy points away from 3D point cloud data and to maintain the sharp features of the model simultaneously,a feature-based weighted fuzzy C-means point cloud denoising algorithm was proposed.Firstly,the point cloud was organized by K-D tree data structure and the large-scale outliers were removed by the statistics of r radius neighboring points.Then,the principal component analysis method was adopted to estimate the curvature and normal vector of point cloud data and the patches with distinguished features were identified according to the curvature feature weight.Pursuant to different feature regions,the feature-preserving weighted fuzzy C-means clustering algorithm was adopted to denoise for the patch with rich feature information and the fuzzy C-means clustering algorithm was adapted to denoise for the patch with less feature information,respectively.Finally,a bilateral filter was used to smooth the data set.The algorithm was verified and the experimental results show that the max denoising error is limited to 0.15 ％ of the model size and the min denoising error is limited to 0.03％ of the model size.In conclusion,this approach moves efficiently and precisely the noise with different scales and intensities in point cloud,meanwhile performing a feature-preserving nature.Moreover,it is robust enough to different noise models.关键词
点云去噪/加权模糊C均值/曲率权值/特征保持/双边滤波Key words
point clouds de-noising/weighted fuzzy c-means/curvature weight/feature preserving/bilateral filter分类
信息技术与安全科学引用本文复制引用
崔鑫,闫秀天,李世鹏..保持特征的散乱点云数据去噪[J].光学精密工程,2017,25(12):3169-3178,10.基金项目
国家国际科技合作专项资金资助项目(No.2013DFA51360) （No.2013DFA51360）
