In this paper we present a method to iteratively capture the dynamic evolution of a surface from a set of point clouds independently acquired from multi-view videos. This is done by deforming the first reconstructed mesh across the sequence to fit these point clouds while preserving the local rigidity with respect to a reference pose. The deformation of this mesh is guided by control points that are randomly seeded on the surface, and around which direct isometries are locally averaged. These rigid motions are computed by iteratively re-establishing point-to-point associations between the deformed mesh and the target data in a way inspired by ICP. Our method introduces a way to account for the point dynamics when establishing these correspondences, a higher level rigidity model between the control points and a coarse-to-fine strategy that allows to fit the temporally inconsistent data more precisely. We tested our approach on known dataset obtained from real video sequences and provide both qualitative and quantitative analysis of our results.     «

In this paper we present a method to iteratively capture the dynamic evolution of a surface from a set of point clouds independently acquired from multi-view videos. This is done by deforming the first reconstructed mesh across the sequence to fit these point clouds while preserving the local rigidity with respect to a reference pose. The deformation of this mesh is guided by control points that are randomly seeded on the surface, and around which direct isometries are locally averaged. These ri...     »