Deformable Shape Registration from Inexact Correspondences

Registration of deformable 3D shapes is a fundamental problem for several applications of computer vision including shape analysis, object detection, tracking, etc. Existing methods often require initial correspondences to match shapes with large deformations. However state-of-the-art geometric models fail when the initial correspondence set is not accurate. In this paper, we present a framework which is able to perform dense registration of pairwise shapes from inexact correspondence set. The proposed method is based on two stage refinements. In the initial stage we consider the local geometrical structure of the shapes and extract the surface patches based on shortest path algorithm from the initial correspondences. The surface patches are then rigidly aligned by a global optimization algorithm, which is further refined by computing functional map on the local structure. Transformed local patches are re-initialized to perform the next stage map computation at the global level. We demonstrate the effectiveness of the approach by performing experiments on standard datasets by comparing with several state-of-the-art methods.