  Accurate and robust object pose estimation for robotics applications requires
verification and refinement steps. In this work, we propose to integrate
hypotheses verification with object pose refinement guided by physics
simulation. This allows the physical plausibility of individual object pose
estimates and the stability of the estimated scene to be considered in a
unified optimization. The proposed method is able to adapt to scenes of
multiple objects and efficiently focuses on refining the most promising object
poses in multi-hypotheses scenarios. We call this integrated approach VeREFINE
and evaluate it on three datasets with varying scene complexity. The generality
of the approach is shown by using three state-of-the-art pose estimators and
three baseline refiners. Results show improvements over all baselines and on
all datasets. Furthermore, our approach is applied in real-world grasping
experiments and outperforms competing methods in terms of grasp success rate.
Code is publicly available at github.com/dornik/verefine.

Computer Vision and Pattern Recognition;Robotics