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Modelling Dynamic Scenes by Registrating Multi-View Image Sequences

Jean-Philippe Pons 1 Renaud Keriven Olivier Faugeras 
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : We present a new variational method for multi-view stereovision and non-rigid three-dimensional motion estimation from multiple video sequences. Our method minimizes the prediction error of the estimated shape and motion. Both problems then translate into a generic image registration task. The latter is entrusted to a similarity measure chosen depending on imaging conditions and scene properties. In particular, our method can be made robust to appearance changes due to non-Lambertian materials and illumination changes. Our method results in a simpler, more flexible, and more efficient implementation than existing deformable surface approaches. The computation time on large datasets does not exceed thirty minutes. Moreover, our method is compliant with a hardware implementation with graphics processor units. Our stereovision algorithm yields very good results on a variety of datasets including specularities and translucency. We have successfully tested our scene flow algorithm on a very challenging multi-view video sequence of a non-rigid event.
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Submitted on : Friday, May 19, 2006 - 9:11:45 PM
Last modification on : Thursday, March 17, 2022 - 10:08:32 AM
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  • HAL Id : inria-00070679, version 1



Jean-Philippe Pons, Renaud Keriven, Olivier Faugeras. Modelling Dynamic Scenes by Registrating Multi-View Image Sequences. RR-5321, INRIA. 2004, pp.20. ⟨inria-00070679⟩



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