A multiresolution-MRF approach for stereo dense disparity estimation

We present a method that reduces the computational cost of the MRF-based stereo algorithm and increases the quality of the final disparity map. In a first step, using window-based method we compute successive disparity maps at different resolutions by varying the correlation window size, in order to estimate for each pixel the set of most probable disparity values. Thus, by replacing the initial disparity range - which may exceed hundreds of pixels for some applications - by the small set of valid disparities, we increase the probability of choosing the right value for each pixel and thus speed up the MRF optimization process.