基于邻域曲率的分支定界点云配准方法
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摘要
点云配准是点云驱动图形学中的重要问题,其配准精度与效率直接影响后期的建模.针对多视角点云模型结合邻域曲率特征和分支定界方法提出一种新的点云配准算法.引入归一化互相关系数度量点云邻域曲率相似度,构造匹配点数组.并用最小二乘模型求取点云配准变换参数得到初始配准参数;通过分支定界法进行精准配准以得到全局最优解.实验表明该算法对于曲率变化显著的点云能够快速收敛,并且能够保证全局最优解.
Point cloud registration plays an important role in points-drive computer graphics as it affects modling quality directly.Aiming at the registration of multi-view point cloud data,It proposes a new registration method based on Branch and Bound(BNB)point cloud algorithm and neighborhood curvature features.The method introduces a new Zero-mean Normalized Cross-correlation Coefficient(ZNCC)to measure curvature similarity of the neighborhood of a point.The initial matching points is built.The transformation parameters of point cloud registration are obtained by the least square model,and the initial registration parameters are obtained.The global optimum solution is obtained by branch and bound method.The results show that the proposed algorithm can converge quickly to point clouds with apparent curvature features,and get the global optimum solution.
Point cloud registration plays an important role in points-drive computer graphics as it affects modling quality directly.Aiming at the registration of multi-view point cloud data,It proposes a new registration method based on Branch and Bound(BNB)point cloud algorithm and neighborhood curvature features.The method introduces a new Zero-mean Normalized Cross-correlation Coefficient(ZNCC)to measure curvature similarity of the neighborhood of a point.The initial matching points is built.The transformation parameters of point cloud registration are obtained by the least square model,and the initial registration parameters are obtained.The global optimum solution is obtained by branch and bound method.The results show that the proposed algorithm can converge quickly to point clouds with apparent curvature features,and get the global optimum solution.
引文
[1]李波,李智,王祥凤.基于多角度点云数据的快速配准方法[J].微电子学与计算机,2017,34(2):123-127.
[2]赵沁平,周彬.虚拟现实研究综述与趋势[C]∥中国计算机协会文集,2016.北京.机械工业出版社,2016:369-392.
[3]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]周明全,耿国华.基于有界旋转角的点云配准算法[J].微电子学与计算机.2017,34(3):46-49.
[5]薛耀红,梁学章,马婷,等.扫描点云的一种自动配准方法[J].计算机辅助设计与图形学学报,2011,23(2):223-231.
[6]张梅,文静华.归一化互相关系数与迭代最近曲面片点云配准方法[J].计算机工程,2016,42(10):271-276.
[7]刘宇.基于微分信息的散乱点云拼合和分割[D].武汉:华中科技大学,2008:70-74.
[8]Yang J,Li H,Campbell D,et al.Go-ICP:A globally optimal solution to 3DICP point-set registration[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2016,38(11):2241-2254.
[9]张梅,文静华,杨滋荣.复杂曲面物体多视角激光点云3D建模关键技术研究[M].北京:科学出版社,2016.
[10]克利夫兰州立大学.微分几何及其应用[M].2版.陈智奇,译.北京:机械工业出版社,2006.
[11]刘通,罗天男,乔立岩,等.基于分支限界的三维曲面全局配准方法[J].仪器仪表学报,2016,37(8):1869-1877.
[2]赵沁平,周彬.虚拟现实研究综述与趋势[C]∥中国计算机协会文集,2016.北京.机械工业出版社,2016:369-392.
[3]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]周明全,耿国华.基于有界旋转角的点云配准算法[J].微电子学与计算机.2017,34(3):46-49.
[5]薛耀红,梁学章,马婷,等.扫描点云的一种自动配准方法[J].计算机辅助设计与图形学学报,2011,23(2):223-231.
[6]张梅,文静华.归一化互相关系数与迭代最近曲面片点云配准方法[J].计算机工程,2016,42(10):271-276.
[7]刘宇.基于微分信息的散乱点云拼合和分割[D].武汉:华中科技大学,2008:70-74.
[8]Yang J,Li H,Campbell D,et al.Go-ICP:A globally optimal solution to 3DICP point-set registration[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2016,38(11):2241-2254.
[9]张梅,文静华,杨滋荣.复杂曲面物体多视角激光点云3D建模关键技术研究[M].北京:科学出版社,2016.
[10]克利夫兰州立大学.微分几何及其应用[M].2版.陈智奇,译.北京:机械工业出版社,2006.
[11]刘通,罗天男,乔立岩,等.基于分支限界的三维曲面全局配准方法[J].仪器仪表学报,2016,37(8):1869-1877.