基于方法库的点云去噪与精简算法
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摘要
为了减少不同尺度噪声对三维点云模型重建效果的影响,提出一种基于直通滤波、统计滤波、半径滤波、改进的双边滤波、体素栅格滤波的方法库的点云模型去噪与精简算法。首先利用直通滤波将目标物体提取出来,再依据噪声点离模型主体的距离,将其分为小尺度噪声和大尺度噪声,然后利用统计滤波结合半径滤波去除大尺度噪声,利用改进的双边滤波去除小尺度噪声,最后通过体素栅格滤波进行点云精简来降低空间复杂度,并以三角网格面重建展示该算法的精度效果。实验结果表明,该算法可有效去除点云模型的不同尺度噪声,在不破坏点云本身几何结构的前提下,保证点云精简的均匀化,而且算法执行速度快,重建效率高。
In order to reduce the influence of different scales of noise on the reconstruction of three-dimensional point cloud models,a denoising and simplification algorithm based on the method library of the passthrough filter,statistical filter,radius filter,improved bilateral filter and voxel grid filter is proposed.Firstly,the object is extracted by the passthrough filter.Then according to the distance between the noise points and the model body,the noise points are divided into the small scale noise and the large scale noise.The large scale noise is removed by the statistical filter and the radius filter,and the small scale noise is removed by the improved bilateral filter.Finally,the three-dimensional point cloud is simplified by the voxel grid filter to reduce the space complexity and the accuracy of the proposed algorithm is shown via the triangular mesh reconstruction.The experimental results show that the proposed algorithm can effectively remove different scales of noise existing in the point cloud model and ensure the uniformity of point cloud simplification under the precondition of not destroying the geometrical structure of the point cloud.In addition,this algorithm runs quickly and has high reconstruction efficiency.
In order to reduce the influence of different scales of noise on the reconstruction of three-dimensional point cloud models,a denoising and simplification algorithm based on the method library of the passthrough filter,statistical filter,radius filter,improved bilateral filter and voxel grid filter is proposed.Firstly,the object is extracted by the passthrough filter.Then according to the distance between the noise points and the model body,the noise points are divided into the small scale noise and the large scale noise.The large scale noise is removed by the statistical filter and the radius filter,and the small scale noise is removed by the improved bilateral filter.Finally,the three-dimensional point cloud is simplified by the voxel grid filter to reduce the space complexity and the accuracy of the proposed algorithm is shown via the triangular mesh reconstruction.The experimental results show that the proposed algorithm can effectively remove different scales of noise existing in the point cloud model and ensure the uniformity of point cloud simplification under the precondition of not destroying the geometrical structure of the point cloud.In addition,this algorithm runs quickly and has high reconstruction efficiency.
引文
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[2]Ge B Z,Zhao D,Tian Q G,et al.Three-dimensional human face laser scanning based on three-line structured light[J].Chinese Journal of Lasers,2010,37(6):1594-1598.葛宝臻,赵丹,田庆国,等.三光带激光三维人脸扫描方法[J].中国激光,2010,37(6):1594-1598.
[3]Fleishman S,Drori I,Cohen-Or D.Bilateral mesh denoising[J].ACM Transactions on Graphics,2003,22(3):950-953.
[4]Jones T R,Durand F,Desbrun M.Non-iterative,feature-preserving mesh smoothing[J].ACM Transactions on Graphics,2003,22(3):943-949.
[5]Yang Z X,Xiao D F.A systemic point-cloud de-noising and smoothing method for 3Dshape reuse[C].Proceedings of the 2012 12th International Conference on Control,Automation,Robotics and Vision,2012:1722-1727.
[6]Su Z X,LI Z Y,Wang X C.Denoising of pointsampled model based on normal modification and median filtering[J].Journal of Computer Aided Design&Computer Graphics,2010,22(11):1892-1898.苏志勋,栗志扬,王小超.基于法向修正及中值滤波的点云平滑[J].计算机辅助设计与图形学学报,2010,22(11):1892-1898.
[7]Cao S,Yue J P,Ma W.Bilateral filtering denoise algorithm for point cloud based on feature selection[J].Journal of Southeast University:Natural Science Edition,2013,43(S2):351-354.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(S2):351-354.
[8]Yuan H,Pang J K,Mo J W.Denoising algorithm for bilateral filtered point cloud based on noise classification[J].Journal of Computer Applications,2015,35(8):2305-2310.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.
[9]Zhang X M,Yu X G,Wan W G,et al.The simplification of 3Dcolor point cloud based on voxel[C]//Proceedings of IET International Conference on Smart and Sustainable City,2013:251-254.
[10]Su B Y,Ma J Y,Peng Y S,et al.Algorithm for RGBD point cloud denoising and simplification based on K-means clustering[J].Journal of System Simulation,2016,28(10):2329-2334.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):2329-2334.
[11]Chen Z W,Da F P.3D point cloud simplification algorithm based on fuzzy entropy iteration[J].Acta Optica Sinica,2013,33(8):0815001.陈璋雯,达飞鹏.基于模糊熵迭代的三维点云精简算法[J].光学学报,2013,33(8):0815001.
[12]Bo F X.Research on 3Dreconstruction system based on Kinect and PCL[D].Shenyang:Northeastern University,2013:44-45.薄夫祥.基于Kinect与PCL的三维重建系统研究[D].沈阳:东北大学,2013:44-45.
[13]Li R Z,Yang M,Liu Y Y,et al.Uniform simplification algorithm of scattered point cloud[J].Acta Optica Sinica,2017,37(7):0710002.李仁忠,杨曼,刘阳阳,等.一种散乱点云的均匀精简方法[J].光学学报,2017,37(7):0710002.
[14]Wiemann T,Kai L,Hertzberg J.Optimizing triangle mesh reconstructions of planar environments[J].IFAC-Papers On Line,2016,49(15):218-223.
[15]Gao X M.Research and implementation of triangular mesh processing algorithm for scattered point cloud model[D].Nanjing:Nanjing Normal University,2011:32-57.高向敏.散乱点云模型三角网格化处理算法的研究与实现[D].南京:南京师范大学,2011:32-57.