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arXiv · 2004.08227

MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models

Siddharth Tourani·Alexander Shekhovtsov·Carsten Rother·Bogdan Savchynskyy
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Abstract

Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to the low-performing solver, the Max Product Linear Programming (MPLP) algorithm, we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin, including the state-of-the-art solver Tree-Reweighted Sequential (TRWS) message-passing algorithm. Additionally, our solver is highly parallel, in contrast to TRWS, which gives a further boost in performance with the proposed GPU and multi-thread CPU implementations. We verify the superiority of our algorithm on dense problems from publicly available benchmarks, as well, as a new benchmark for 6D Object Pose estimation. We also provide an ablation study with respect to graph density.

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Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy. 2020-04-16. MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. https://arxiv.org/abs/2004.08227

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