Global priors for binocular stereopsis

Develops a Bayesian feedback method for incorporating global structure into prior models for binocular stereopsis. Since most stereo scenes contain either background continuation (large background surfaces continuing behind smaller fore-ground surfaces) or transparency continuation (small opaque patches on a transparent surface), highly nonlocal interactions are often present in the scene geometry. The commonly used local prior models which impose piecewise smoothness constraints on the reconstructions do not capture the probabilistic subtleties of global 3D structures. Therefore, the authors develop a hybridized prior which balances the local properties of the scene geometry with the global properties. Experimental results demonstrating the potential of this technique are provided