混合滤波与改进双边滤波的点云去噪算法

Point Cloud Denoising Algorithm Based on Hybrid Filtering and Improved Bilateral Filtering

激光扫描获取的曲面零件点云中包含的噪声点将影响零件的曲面拟合精度.提出了一种根据噪声分类分步降噪及光顺方法,利用kd tree结合K-means聚类算法去除离群点噪声,引入采样点曲率改进双边滤波因子,对非离群点噪声进行光顺.利用该方法对液力变矩器导轮叶片点云进行去噪,并与利用三坐标测量机接触式测量获得的叶片局部测量结果配准比较.实验结果表明,70%以上的接触式扫描测量点与利用本文方法去噪后的叶片点云之间的偏差在±10μm之间.表明该方法可以很好地保留扫描物体的几何特征,获得的点云模型能够满足后续模型重建的精度要求.

The point cloud of curved parts obtained by laser scanning contains noise points,that may affect the surface fitting accuracy of the parts.A denoising and smoothing method based on noise classification was proposed.The kd tree algorithm and K-means clustering algorithm were combined to remove the outlier noise.The curvature of the sampling points was substituted to improve the bilateral filter factor,and the improved bilateral filtering algorithm was used to smooth the non-outlier noise.The proposed method was used to denoise the point cloud of the blade of the guide pulley of the hydraulic torque converter,and compared with the measurement results obtained by coordinate measuring machine.The experimental results showed that the deviations between more than 70%of the measurement points and the denoised point cloud is within±10μm.It was found that the proposed method can well preserve the geometric features of the scanned object,and the denoised point cloud can meet the accuracy requirements of the model reconstruction.