Geodesic-Based Bayesian Coherent Point Drift
Geodesic-Based Bayesian Coherent Point Drift
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DOI：
10.1109/tpami.2022.3214191
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发表时间：
2022-10
影响因子：
23.6
通讯作者：
Osamu Hirose
中科院分区：
文献类型：
--
作者：
Osamu Hirose
相干点漂移是一种众所周知的非刚性配准算法，即变形一个形状以匹配另一个形状的过程。尽管它很流行，但该算法有一个主要的缺点尚未解决：当一个形状的不同部分彼此相邻时，它会不自然地变形，例如，人的腿。不适当的变形源于基于近似的变形约束，称为运动相干性。本研究提出一种非刚性配准方法来解决这一缺陷。解决这个问题的关键是使用测地线重新定义运动相干性，即形状表面上点之间的最短路径。我们还提出了配准方法的加速变体。在数值研究中，我们证明了该算法可以克服相干点漂移的缺点。我们还证明了加速算法可以应用于包含数百万个点的形状。
Coherent point drift is a well-known algorithm for non-rigid registration, i.e., a procedure for deforming a shape to match another shape. Despite its prevalence, the algorithm has a major drawback that remains unsolved: It unnaturally deforms the different parts of a shape, e.g., human legs, when they are neighboring each other. The inappropriate deformations originate from a proximity-based deformation constraint, called motion coherence. This study proposes a non-rigid registration method that addresses the drawback. The key to solving the problem is to redefine the motion coherence using a geodesic, i.e., the shortest route between points on a shape's surface. We also propose the accelerated variant of the registration method. In numerical studies, we demonstrate that the algorithms can circumvent the drawback of coherent point drift. We also show that the accelerated algorithm can be applied to shapes comprising several millions of points.