Non-rigid point set registration: Coherent Point Drift
Non-rigid point set registration: Coherent Point Drift
复制标题
DOI：
10.7551/mitpress/7503.003.0131
复制
发表时间：
2006-12
期刊：
影响因子：
--
通讯作者：
A. Myronenko;Xubo B. Song;M. A. Carreira-Perpiñán
中科院分区：
文献类型：
--
作者：
A. Myronenko;Xubo B. Song;M. A. Carreira-Perpiñán
提出了一种新的点集非刚性配准的概率方法相干点漂移（CPD）。该配准被视为速度场上具有运动相干性约束的最大似然（ML）估计问题，使得一个点集以相干性移动以与第二个点集对齐。我们提出了运动相干性约束，并通过变分方法推导了正则化ML估计的解，从而得到了一个优雅的核形式。我们还推导了带有确定性退火的惩罚式机器学习优化的EM算法。CPD方法不需要对变换模型进行任何先验假设，只需要对运动相干性进行假设，即可同时找到非刚性变换和两个点集之间的对应关系。该方法可以估计复杂的非线性非刚性变换，并且在二维和三维例子中都是准确的，并且在存在异常点和缺失点的情况下具有鲁棒性。
We introduce Coherent Point Drift (CPD), a novel probabilistic method for non-rigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the non-rigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex non-linear non-rigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.