面向反光工件点云缺陷的点云增强算法
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摘要
针对反光工件的三维扫描点云存在的缺陷以及点云处理时间长的问题,提出一种点云增强的算法以改善缺陷点云的质量、缩短点云处理的时间,从而提高点云三维重建的精度和效率.首先输入大量的、分布不均匀且包含离群点和噪声的无组织点云,使用基于kD-tree的点云均匀重采样将点云数据分成多个子集;然后使用改进的局部最优投影算法对点云进行降噪处理;最后使用边缘感知重采样对点云进行增采样,达到保留和增强点云特征的效果.实验结果证明,该算法的处理是有效的.
Considering the problems of point cloud defects of reflective workpiece through three-dimensional scanning process and long processing time of the point cloud, this paper proposes a point cloud consolidation algorithm to improve the quality of the scanned three-dimensional data and to reduce its processing time. The algorithm can thus improve the accuracy and efficiency of the three-dimensional reconstruction. The first step of the algorithm was to acquire the unorganized point cloud with nonuniform distribution, outliers and noise, then a kD-tree based resampling method was used to subdivide the points into multiple subsets. For each of the point subset, noise reduction was performed through an improved local optimal projection algorithm. Finally, the edge-aware resampling method was adopted to upsample the points to preserve and consolidate the feature of the point cloud. Experimental results show that the point cloud consolidation algorithm is effective.
Considering the problems of point cloud defects of reflective workpiece through three-dimensional scanning process and long processing time of the point cloud, this paper proposes a point cloud consolidation algorithm to improve the quality of the scanned three-dimensional data and to reduce its processing time. The algorithm can thus improve the accuracy and efficiency of the three-dimensional reconstruction. The first step of the algorithm was to acquire the unorganized point cloud with nonuniform distribution, outliers and noise, then a kD-tree based resampling method was used to subdivide the points into multiple subsets. For each of the point subset, noise reduction was performed through an improved local optimal projection algorithm. Finally, the edge-aware resampling method was adopted to upsample the points to preserve and consolidate the feature of the point cloud. Experimental results show that the point cloud consolidation algorithm is effective.
引文
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[22]Rusu R B,Marton Z C,Blodow N,et al.Towards 3D point cloud based object maps for household environments[J].Robotics and Autonomous Systems,2008,56(11):927-941
[23]Cignoni P,Callieri M,Corsini M,et al.MeshLab:an opensource mesh processing tool[C]//Proceedings of Eurographics Italian Chapter Conference.Aire-la-Ville:Eurographics Association Press,2008:129-136
[2]Rusu R B,Cousins S.3D is here:point cloud library(PCL)[C]//Proceedings of the IEEE International Conference on Robotics and Automation.Los Alamtios:IEEE Computer Society Press,2011:1-4
[3]Wu Longhua,Huang Hui.Survey on points-driven computer graphics[J].Journal of Computer-Aided Design&Computer Graphics,2015,27(8):1341-1353(in Chinese)(伍龙华,黄惠.点云驱动的计算机图形学综述[J].计算机辅助设计与图形学学报,2015,27(8):1341-1353)
[4]Chen F,Brown G M,Song M.Overview of 3D shape measurement using optical methods[J].Optical Engineering,2000,39(1):10-22
[5]Kobbelt L,Botsch M.A survey of point-based techniques in computer graphics[J].Computers&Graphics,2004,28(6):801-814
[6]Wang Y T,Feng H Y.Modeling outlier formation in scanning reflective surfaces using a laser stripe scanner[J].Measurement,2014,57:108-121
[7]K?hler J,N?ll T,Reis G.,et al.Robust outlier removal from point clouds acquired with structured light[C]//Proceedings of Eurographics.Aire-la-Ville:Eurographics Association Press,2012:21-24
[8]Alexa M,Behr J,Cohen-Or D,et al.Point set surfaces[C]//Proceedings of the Conference on Visualization.Los Alamtions:IEEE Computer Society Press,2001:21-28
[9]Fleishman S,Cohen-Or D,Silva C T.Robust moving least-squares fitting with sharp features[J].ACM Transactions on Graphics,2005,24(3):544-552
[10]Zhang Xin,Wang Zhangye,Fan Hanqi,et al.A feature preserving denoising approach for scanned models based on trilateral filtering[J].Journal of Computer-Aided Design&Computer Graphics,2009,21(7):936-942(in Chinese)(张鑫,王章野,范涵奇,等.保特征的三维模型的三边滤波去噪算法[J].计算机辅助设计与图形学学报,2009,21(7):936-942)
[11]Lipman Y,Cohen-Or D,Levin D,et al.Parameterization-free projection for geometry reconstruction[C]//Computer Graphics Proceedings,Annual Conference Series,ACM SIGGRAPH.New York:ACM Press,2007:Article No.22
[12]Huang H,Li D,Zhang H,et al.Consolidation of unorganized point clouds for surface reconstruction[J].ACM Transactions on Graphics,2009,28(5):Article No.176
[13]Liao B,Xiao C X,Jin L Q,et al.Efficient feature-preserving local projection operator for geometry reconstruction[J].Computer-Aided Design,2013,45(5):861-874
[14]Preiner R,Mattausch O,Arikan M,et al.Continuous projection for fast L1 reconstruction[J].ACM Transactions on Graphics,2014,33(4):Article No.47
[15]Liu S J,Chan K C,Wang C C L.Iterative consolidation of unorganized point clouds[J].IEEE Computer Graphics and Applications,2012,32(3):70-83
[16]Lange C,Polthier K.Anisotropic smoothing of point sets[J].Computer Aided Geometric Design,2005,22(7):680-692
[17]Adamson A,Alexa M.Point-sampled cell complexes[J].ACMTransactions on Graphics,2006,25(3):671-680
[18]Avron H,Sharf A,Greif C,et al.?1-Sparse reconstruction of sharp point set surfaces[J].ACM Transactions on Graphics,2010,29(5):Article No.135
[19]Calderon S,Boubekeur T.Point morphology[J].ACM Transactions on Graphics,2014,33(4):Article No.45
[20]Bentley J L.Multidimensional binary search trees used for associative searching[J].Communications of the ACM,1975,18(9):509-517
[21]Huang H,Wu S H,Gong M L,et al.Edge-aware point set resampling[J].ACM Transactions on Graphics,2013,32(1):Article No.9
[22]Rusu R B,Marton Z C,Blodow N,et al.Towards 3D point cloud based object maps for household environments[J].Robotics and Autonomous Systems,2008,56(11):927-941
[23]Cignoni P,Callieri M,Corsini M,et al.MeshLab:an opensource mesh processing tool[C]//Proceedings of Eurographics Italian Chapter Conference.Aire-la-Ville:Eurographics Association Press,2008:129-136