Rigid and non-rigid 3D motion estimation from multiview image sequences

Multiview image sequence processing has been the focus of considerable attention in recent literature. This paper presents an efficient technique for object-based rigid and non-rigid 3D motion estimation, applicable to problems occurring in multiview image sequence coding applications. More specifically, a neural network is formed for the estimation of the rigid 3D motion of each object in the scene, using initially estimated 2D motion vectors corresponding to each camera view. Non-linear error minimization techniques are adopted for neural network weight update. Furthermore, a novel technique is also proposed for the estimation of the local non-rigid deformations, based on the multiview camera geometry. Experimental results using both stereoscopic and trinocular camera setups illustrate and evaluate the proposed scheme.