基于邻域平均法的点云去噪算法研究
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摘要
针对目前离散点云数据中出现的起伏变化剧烈的噪声点难以正确去除的问题,提出了一种改进的去噪算法.首先利用空间单元格技术对当前点云进行划分,确定其空间拓扑关系;然后基于每个单元格内的点确定各个单元格的"中心点";最后根据距离标准偏差判断是否为噪声点.实验结果表明,以少量"中心点"代替空间单元格内所有点进行噪声去除的思想,可以加快运算速度,提高效率;该算法不仅能够有效的去除噪声点,而且能够较好的保留点云模型的细节特征.
Aiming at the problem that it is hard to correctly remove the noise point which fluctuates drastically in the current discrete point cloud data, an improved denoising algorithm is proposed. Firstly, the current point cloud is divided by space cell technology to determine its topological relationship. Then the "center point" is calculated based on the number of points in each cell to find the average distance. Finally, the noise point is determined according to the distance standard deviation. The experimental results show that the idea of replacing all the points in the space cell with a small amount of "center point" to remove the noise can speed up the operation and improve the efficiency. The algorithm can not only remove the noise point effectively, but also retains the detail characteristics of the point cloud model.
Aiming at the problem that it is hard to correctly remove the noise point which fluctuates drastically in the current discrete point cloud data, an improved denoising algorithm is proposed. Firstly, the current point cloud is divided by space cell technology to determine its topological relationship. Then the "center point" is calculated based on the number of points in each cell to find the average distance. Finally, the noise point is determined according to the distance standard deviation. The experimental results show that the idea of replacing all the points in the space cell with a small amount of "center point" to remove the noise can speed up the operation and improve the efficiency. The algorithm can not only remove the noise point effectively, but also retains the detail characteristics of the point cloud model.
引文
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[15]张毅,刘旭敏,隋颖,等.基于K-近邻点云去噪算法的研究与改进[J].计算机应用,2009,29(4):1011-1014.
[16]聂建辉,胡英,马孜.散乱点云离群点的分类识别算法[J].计算机辅助设计与图形学学报,2011,23(9):1526-1532.
[2]熊晓光,王毅.激光雷达点云数据截面提取计算方法研究[J].江西理工大学学报,2013,34(3):42-46.
[3]梁士超,韩永国,吴亚东.改进的核密度点云去噪算法的研究与实现[J].计算机工程与设计,2015,36(5):1285-1289.
[4]曹爽,岳建平,马文.基于特征选择的双边滤波点云去噪算法[J].东南大学学报(自然科学版),2013,43(增刊2):351-354.
[5]孙正林,邹峥嵘,吴爱琴.一种改进的Mean Shift点云数据滤波[J].测绘工程,2011,20(5):57-59.
[6]Andrei C.Efficient surface reconstruction from noisy data using regularized membrane pote-ntials[J].IEEE Transaction on Image Processing,2009,18(5):1119-1134.
[7]Fleishman S,Drori I,Cohen-Or D.Bilateral mesh denoising[J].ACM Transactions on Graphics(TOG),2003,22(3):950-953.
[8]Shachar F,Iddo D.Bilateral mesh denoising[J].Computer Science,2004,19(8):169-175.
[9]李仁忠,杨曼,冉媛,等.基于方法库的点云去噪与精简算法[J].激光与光电子学进展,2018(1):245-251.
[10]朱俊锋,胡翔云,张祖勋,等.多尺度点云噪声检测的密度分析法[J].测绘学报,2015,44(3):282-291.
[11]苏本跃,马金宇,彭玉升,等.基于K-means聚类的RGBD点云去噪和精简算法[J].系统仿真学报,2016,28(10):2329-2335.
[12]袁华,庞建铿,莫建文.基于噪声分类的双边滤波点云去噪算法[J].计算机应用,2015,35(8):2305-2310.
[13]刘大峰,廖文和,戴宁,等.散乱点云去噪算法的研究与实现[J].东南大学学报(自然科学版),2007,37(6):1108-1112.
[14]柳红凯,徐昌荣,徐晓.基于渐进加密三角网机载LIDAR点云滤波改进算法研究[J].江西理工大学学报,2016,37(3):50-55.
[15]张毅,刘旭敏,隋颖,等.基于K-近邻点云去噪算法的研究与改进[J].计算机应用,2009,29(4):1011-1014.
[16]聂建辉,胡英,马孜.散乱点云离群点的分类识别算法[J].计算机辅助设计与图形学学报,2011,23(9):1526-1532.