Increasing the Efficiency of Local Stereo by Leveraging Smoothness Constraints

We introduce a novel framework for efficient stereo disparity estimation leveraging the spatial smoothness typically assumed in stereo and formalized by the various smoothness constraints. The smoothness constraint presumes that a neighboring set of pixels shares the same disparity or the disparity varies smoothly. Our key insight is that it hence suffices to evaluate any single one of those pixels at the correct disparity to identify a valid estimate for the entire set. We leverage this insight into the formulation of a complexity reducing mechanism. We distribute the exploration of the disparity search space among neighboring pixels, effectively reducing the set of disparity hypothesis evaluated at each individual pixel. Moreover, we integrate a recently proposed concept to deploy sparsity within this neighborhood of distributed disparities into our novel mechanism, in order to further reduce the computational burden. Our experiments clearly demonstrate the effectiveness of our approach by achieving comparable results to the baseline of exhaustive disparity search. The analysis of the computational complexity of our proposed mechanisms illustrates how, by making moderate assumptions on the smoothness of the observed scene, we can reduce the computational complexity of local stereo disparity search by upwards of two orders of magnitude while maintaining the comparable result quality.