Dense and Accurate Spatio-Temporal Multi-View Stereovision

Abstract. In this paper, we propose a novel method to simultaneously and accurately estimate the 3D shape and 3D motion of a dynamic scene from multiple-viewpoint calibrated videos. We follow a variational ap-proach in the vein of previous work on stereo reconstruction and scene flow estimation. We adopt a representation of a dynamic scene by an an-imated mesh, i.e. a polygonal mesh with fixed connectivity whose time-varying vertex positions sample the trajectories of material points. In-terestingly, this representation ensures a consistent coding of shape and motion by construction. Our method accurately recovers 3D shape and 3D motion by optimizing the positions of the vertices of the animated mesh. This optimization is driven by an energy function which incor-porates multi-view and inter-frame photo-consistency, smoothness of the spatio-temporal surface and of the velocity field. Central to our work is an image-based photo-consistency score which can be efficiently computed and which fully handles projective distortion and partial occlusions. We demonstrate the effectiveness of our method on several challenging real-world dynamic scenes. Key words: Spatio-temporal stereovision, Scene flow, Motion capture