MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
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.
Cite
@article{arxiv.2004.08227,
title = {MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models},
author = {Siddharth Tourani and Alexander Shekhovtsov and Carsten Rother and Bogdan Savchynskyy},
journal= {arXiv preprint arXiv:2004.08227},
year = {2020}
}
Comments
Accepted in ECCV-2018