采用密度k-means和改进双边滤波的点云自适应去噪算法

Points cloud self-adaptive denoising algorithm based on density k-means and improved bilateral fitering

采用相移结构光测量系统得到的三维点云,不可避免存在噪声。通过密度k均值(k-means)聚类算法将点云分为大尺度噪声点和小尺度噪声点,设定邻域大小以及点的数量来去除孤立噪声点;使用类内距离和类间距离的比值作为评价函数,得到最佳聚类数去除小片噪声点云;对于混杂在真实点云中的小尺度噪声点,采用鲁棒性更强的改进型双边滤波器进行点云光顺。实验验证表明:采用基于密度k-means和改进双边滤波结合的点云去噪算法可以有效去除各类噪声点,保持点云特征,相比平均曲率算法和基于特征选择的双边滤波算法,去噪效率分别提高了24%和16%。

3D points cloud gained by using phase-shift measurement system unavoidably has noise. Cloud-point is divided into large scale noise points and small scale noise points by density k-means clustering algorithm,isolated noise points is removed by setting neighbourhood size and quantity of points; take ratio of intraclass distance and between-class distance as evaluation function obtain the optimal number of clusters to remove little noise points cloud. Using stronger robustness improved bilateral filterer for points cloud. Experiment shows that point clouds denoising algorithm combines density k-means and improved bilateral filtering can effectively remove noises point,maintain point cloud features. Compared to mean curvature algorithm and the bilateral filtering algorithm based on feature selection,the de-noising efficiency are improved around 24 % and 16 %.