Hardware-efficient stereo estimation using a residual-based approach

Many promising embedded computer vision applications, such as stereo estimation, rely on inference computation on Markov Random Fields (MRFs). Sequential Tree-Reweighted Message passing (TRW-S) is a superior MRF solving method, which provides better convergence and energy than others (e.g., belief propagation). Since software TRW-S solvers are slow, custom TRW-S hardware has been proposed to improve execution efficiency. This paper proposes hardware mechanisms to further optimize TRW-S hardware efficiency, by tracking differences in input message values (residues) and skipping computation when values no longer change (residue is zero). Evaluations of our hardware mechanisms using Middlebury benchmark show 1.6x to 6x potential reduction in computation (depending on design parameters) while increasing energy by only 0.4% to 4.8%.