VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement
This work addresses the need for reliable object pose estimation in robotics applications, offering an incremental improvement by combining verification and refinement steps.
The paper tackles the problem of accurate and robust object pose estimation for robotics by integrating hypotheses verification with physics-guided iterative refinement, resulting in improved performance over baselines on three datasets and higher grasp success rates in real-world experiments.
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.