Modelling Dynamic Scenes by Registering Multi-View Image Sequences

In this paper, 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 shape and motion estimates. 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. It 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 o...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Jean-Philippe Pons, Renaud Keriven, Olivier D. Faugeras
• Keywords: Challenging Multi-view Video, Computer Vision, CVPR 2005, Multi-view Stereovision, Non-rigid Three-dimensional Motion, Scene Flow Algorithm, Stereovision Algorithm Yields