Bidirectional Patch-based Correlations with Local Rigidity for Global Nonrigid Registration

The registration of time-varying 3D shapes with high degrees of freedom remains a challenging task. Most existing techniques attempt to address this issue by solving an optimization problem defined on deformation graph with as-rigid-as-possible smoothness prior, which usually struggle to capture large scale displacements. Motivated by the insight that a set of points tends to collectively undergo significant rigid motion accompanied by slight nonrigid deformation, we propose a two-step approach to address nonrigid registration in a coarse-to- fine manner. In the first step, coarse correlations between source and target points are constructed by estimating a set of rigid transformations for local patches which are regional clusters of points. To leverage more contextual information, a bidirectional registration module is introduced that estimates both the forward and backward patch-wise rigid transformation fields (PRTFs). Subsequently, in the second step, the source point set is warped by blending both forward and backward PRTFs and fed into a deformation optimization module. Here, unidirectional point-based correspondences are sought to refine the global nonrigid transformation fields (GNTFs) while adhering to local rigidity constraints. To illustrate the efficacy of our method, we conduct tests on challenging scenarios involving human datasets, including large displacements resulting from fast inter-frame motions or pose changes. Both qualitative and quantitative results demonstrate that our approach outperforms several state-of-the-art methods in terms of robustness and registration accuracy.