基于法矢修正的点云数据去噪平滑算法

Smoothing Algorithm of Point Cloud Based on Normal Vector Correction

逆向工程数据采集点云数据的离群点和噪声点的存在,直接影响数据的多视图拼合,特征提取,数据精简以及曲面重构的质量。在对双边滤波和三边滤波算法的研究的基础上,提出了一种基于法矢修正的点云数据去噪平滑的算法。对于噪声点通过加权协方差矩阵估算点云邻域内几何特征,将具有相似几何特征的点限制在法向量相似的区域,在相似邻域内的采样点法矢和位置分别进行保特征的三边滤波。改进后的算法能够有效地滤出点云数据中的离群点和噪声点,同时保证了点云数据的尖锐及边缘特征,取得良好的去噪效果。

The presence of outliers and noise points in the cloud data of the reverse engineering data collection directly affects the mult-view’s combination of the data,feature extraction,data reduction and the quality of surface reconstruction.Based on the research of bilateral filtering and trilateration filtering algorithm,this paper presents an algorithm of denoising and smoothing of point cloud data based on normal vector correction.Firstly,the local neighborhood of the point cloud data is constructed,and the noise points of the scattered data collected by the data acquisition system are classified and processed.For outliers in the point cloud data,mathematical statistics analysis is used to filter out the points whose KNN is lower than the threshold.The points with similar geometric characteristics are restricted to the regions where the normal vectors are similar,and the normal vectors and positions of the samples in the similar neighborhoods are triangulated.The improved algorithm can effectively filter the outliers and noise points in the point cloud data,and ensure the sharp and edge features of the point cloud data and obtain good denoising effect.