基于局部仿射配准的鲁棒非刚体配准算法

针对传统点集非刚体配准算法对复杂局部形变数据配准精度低，收敛速度慢等问题，该文提出一种基于局部仿射配准的鲁棒非刚体配准算法。该算法采用分层迭代的方式由粗到精地完成点集的非刚体配准。在每层迭代中，首先对子形状点集集合和子目标点集集合进行分块处理并更新分块后每一类子点集的形状控制点。然后利用控制点引导仿射迭代最近点(ICP)算法求解对应子点集间的局部仿射变换。接着利用上一步求解的局部仿射变换，更新子形状点集集合及其形状控制点集合。直到配准误差收敛时，循环结束并输出更新后的形状点集。实验结果表明，所提算法与传统点集非刚体算法相比具有更强的精确性和收敛性。

Robust Non-rigid Registration Algorithm Based on Local Affine Registration

To solve the problem that the traditional point set non-rigid registration algorithm has low precision and slow convergence speed for complex local deformation data, this paper proposes a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the non-rigid registration of the point set from coarse to fine. In each iteration, the sub shape point sets and sub target point sets are divided and the shape control points of each sub point set are updated. Then the control point guided affine Iterative Closest Point (ICP) algorithm is used to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Until the registration error converges, the loop ends and outputs the updated shape point set. Experimental results demonstrate that the accuracy and convergence of the proposed algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.