基于密度聚类和投票判别的三维数据去噪方法

本文介绍的是一种有效发现和去除三维数据噪音方法，它既能改善去除噪音的效果，又能保持特征信息。该方法是一种两阶段噪音数据处理方法。该方法首先通过密度聚类将数据分类为正常簇集合、疑似簇集合、异常簇集合，然后利用正常簇集合中的点对疑似簇集合中各点进行投票判断，最终得到一个合理的三维点云数据模型。实验结果证明，本方法能够有效去除制造类工件模型的三维点云数据中的噪音数据，同时能良好保持模型表面的特征，加快处理效率。

A Denoising Method for Scanned 3D Point Cloud based on Density Clustering and Majority Voting

This paper presents an effective outlier detection and removal method of denosing 3D data, which aims at improving denoising effects as well as reserving characteristic information.. The two stage treatment method is adopted in this paper. Firstly, the data is classified as good clusters, suspicious clusters or outliers clusters according to density clustering. Then the good cluster point determines suspicious clusters by majority voting, which will 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.