Accurate 6D object pose estimation from images is a key problem in object-centric scene understanding, enabling applications in robotics, augmented reality, and scene reconstruction. Despite recent advances, existing methods often produce physically inconsistent pose estimates, hindering their deployment in real-world scenarios. We introduce PhysPose, a novel approach that integrates physical reasoning into pose estimation through a postprocessing optimization enforcing non-penetration and gravitational constraints. By leveraging scene geometry, PhysPose refines pose estimates to ensure physical plausibility. Our approach achieves state-of-the-art accuracy on the YCB-Video dataset from the BOP benchmark and improves over the state-of-the-art pose estimation methods on the HOPE-Video dataset. Furthermore, we demonstrate its impact in robotics by significantly improving success rates in a challenging pick-and-place task, highlighting the importance of physical consistency in real-world applications.
@article{arxiv.2503.23587,
title = {PhysPose: Refining 6D Object Poses with Physical Constraints},
author = {Martin Malenický and Martin Cífka and Médéric Fourmy and Louis Montaut and Justin Carpentier and Josef Sivic and Vladimir Petrik},
journal= {arXiv preprint arXiv:2503.23587},
year = {2025}
}