计算机应用研究2018，Vol.35Issue(2)：619-623,5.DOI:10.3969/j.issn.1001-3695.2018.02.064
基于密度聚类和投票判别的三维数据去噪方法
Denoising method for scanned 3D point cloud based on density clustering and majority voting
摘要
Abstract
This paper presented an effective outlier detection and removal method of denosing 3D data,which aimed at improving denoising effects as well as reserving characteristic information.This paper adopted the two stage treatment method.Firstly,it classified the data as good clusters,suspicious clusters or outliers clusters according to density clustering.Then the good cluster point determined suspicious clusters by majority voting,which would get a reasonable 3D point cloud data model finally.The experimental results show that this method can effectively remove the noise data in the 3D point cloud data and can effectively maintain the characteristics of the model surface and speed up the processing efficiency.关键词
点云数据/异常点检测/基于密度聚类/投票判别算法Key words
point cloud/outlier detection/density based clustering/voting discrimination algorithm分类
信息技术与安全科学引用本文复制引用
陶抒青,刘晓强,李柏岩,Shen Jie..基于密度聚类和投票判别的三维数据去噪方法[J].计算机应用研究,2018,35(2):619-623,5.基金项目
上海市教育委员会科研创新项目(12ZZ060) （12ZZ060）
