多尺度点云噪声的平滑去噪方法  

Smooth Denoising Method for Multi-Scale Point Cloud Noise

作　　者：郑天洋 马斌[1] Zheng Tianyang;Ma Bin(School of Mechanical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)

机构地区：[1]兰州交通大学机电工程学院,甘肃兰州730070

出　　处：《应用激光》2024年第12期148-157,共10页Applied Laser

摘　　要：为了降低三维激光扫描设备获取的三维点云数据中存在的多尺度噪声对后续阶段处理产生的影响,提出了一种基于法向量方向信息特征分类的去噪方法。该算法首先通过统计滤波结合半径滤波去除点云模型中的大尺度噪声,然后利用主成分分析法获得点云的法向量信息,通过局部邻域法向量间的夹角构建法向直方图,根据法向直方图的峰度值对点云进行区域划分,将点云划分为特征细节较少的平面区域和具有丰富特征细节的特征区域。针对不同的区域分别采用双边滤波和自适应引导滤波对点云中的小尺度噪声进行去噪光顺。实验结果表明,本文方法既能有效地去除点云模型的多尺度噪声,也能较好地保持模型的特征细节。t To mitigate the impact of multi-scale noise present in 3D point cloud data acquired through 3D laser scanning on subsequent processing stages,a denoising approach leveraging normal vector direction information for feature classification has been developed.Firstly,the algorithm removes the large-scale noise in the point cloud model by statistical filtering combined with radius filtering,and then uses the principal component analysis method to obtain the normal vector information of the point cloud,constructs the histogram of normal orientations through the angle between the local neighborhood normal vectors,divides the point cloud into a plane area with less feature details and a feature area with rich feature details.For different regions,bilateral filtering and adaptive guided filtering are used to denoise small-scale noise in point clouds.Experimental results show that the proposed method can effectively remove the multi-scale noise of the point cloud model,and also maintain the featuredetails of themodel well.

关 键 词：点云去噪 特征分类 双边滤波 自适应引导滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]