基于最优邻域特征加权的点云引导滤波算法

Point Cloud Guided Filtering Algorithm Based on Optimal Neighborhood Feature Weighting

在点云去噪过程中,当点云数据中的大尺度噪声点被去除后,点云周围通常还混杂着难以直接去除的小尺度噪声点,严重影响重建表面的光滑性,导致模型出现一定程度的特征失真。针对小尺度噪声点,提出了一种基于最优邻域特征加权的点云引导滤波算法。首先基于信息熵函数选取最优初始邻域,结合曲面变化度、法线变化度和距离特征实现特征点识别,然后再对特征点的邻域进行自适应生长以获得平滑邻域,最后利用曲面变化度加权调整引导滤波算法,实现对复杂曲面零件特征和非特征部分的各向异性光顺。实验结果表明,所提算法相较于几种常用的光顺算法对噪声点云的平滑效果更明显,在特征保持方面表现更好,并且在效率方面更优。

In the process of point cloud denoising,after removing largescale noise points from the point cloud data,there are usually small noise points mixed around the point cloud that are difficult to directly remove.This seriously affects the smoothness of the reconstructed surface and leads to a certain degree of feature distortion in the model.Thus,for smallscale noise points,this study proposes a pointcloudguided filtering algorithm based on optimal neighborhood feature weighting.The optimal initial neighborhood is selected based on the information entropy function,and feature points are identified by combining surface and normal variations with distance features.The neighborhoods of the feature points are adaptively grown to obtain a smooth neighborhood.The guided filtering algorithm is adjusted by surface variation weighting to achieve anisotropic smoothness of the feature and nonfeature parts of the complex surface part.As evidenced by experimental results,the proposed algorithm exhibits a more obvious smoothing effect on noisy point clouds,performs better in feature retention,and is significantly more efficient than several commonly used smoothing algorithms.