基于改进DPC和特征分区的点云去噪算法

针对三维激光扫描仪获取到的点云数据存在的多尺度混合噪声将严重影响后续的三维模型重建的问题,提出了一种基于改进的密度峰值聚类算法(DPC)和特征分区的点云去噪算法。首先通过改进的DPC算法去除远离点云主体的大尺度噪声；然后利用主成元分析法(PCA)和曲面变分获取点云法矢及曲率信息,同时采用邻域传播法调整法矢方向并根据曲率对点云进行划分,对特征区域点云与平坦区域点云分别采取自适应双边滤波和正交整体最小二乘平面拟合的方法进行光顺去噪。实验结果表明:在包含混合噪声的bunny与block模型下,利用该算法去噪后点云数据最大误差分别为0235mm和0157mm,平均误差分别为0029mm和0009mm,均能取得较好的去噪效果,且降低了去噪参数设置的复杂性。

Point cloud denoising algorithm based on improved DPC and feature partition

To solve the problem that the multi scale mixed noise of point cloud data obtained by 3D laser scanner seriously affects the subsequent 3D model reconstruction,a point cloud denoising algorithm based on improved density peak clustering algorithm(DPC)and feature partition is proposed.Firstly,the improved DPC algorithm is used to remove the large scale noise far away from the main body of the point cloud.Then,principals of component analysis(PCA)and surface variation are used to obtain the normal vector and curvature information of the point cloud.At the same time,neighborhood propagation method is adopted to adjust the normal vector direction and divide the point cloud according to the curvature.Adaptive bilateral filtering and orthogonal total least squares plane fitting are applied to smooth and denoise the point cloud in feature area and flat area respectively.The experimental results show that under the bunny and block model with mixed noise,the maximum error of the point cloud data is 0.235mm and 0.157mm respectively,and the average error is 0.029mm and 0.009mm respectively,which can achieve good denoising effect and reduce the complexity of parameter setting.