Parallel implementations of a disparity estimation algorithm based on a Proximal splitting method

The Parallel Proximal Algorithm (PPXA+) has been recently introduced as an efficient tool for solving convex optimization problems. It has proved particularly effective in the context of stereo vision, used as the methodological core of a novel disparity estimation technique. In this work, the main methodological issues limiting the efficient parallelization of this technique are addressed, and further modifications are proposed to enable and optimize the design of parallel implementations. Finally, actual implementations that fit both the multi-core CPU and GPU devices are provided and tested to validate the performance potential of the proposed technique.