The problem of reconstructing deformable 3D surfaces has been studied in the non-rigid structure from motion context, where either tracked points over long sequences or an initial 3D shape are required, and also with piecewise methods, where the deformable surface is modeled as a triangulated mesh, which is fitted to an initial estimation of the 3D surface computed from correspondences in two views. In this paper we present a new scheme to reconstruct deformable surfaces by tracking relevant features that parametrize such deformation. Assuming that an initial 3D shape related to a reference frame is available, we initially match the reference and current frames using visual information. Then, these correspondences are clustered in patches with geometric characteristics in the image domain and 3D space. In order to reduce the number of parameters to be estimated, we explain each cluster using thin-plate splines (TPS) with a minimal number of control points. Then the 3D coordinates of these control points in the deformed surface are estimated using a non-linear least squares approach, deriving on the reconstruction of the full deformed patches. We perform experiments in synthetic and real data of monocular video sequences to validate our approach.
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