VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement

TL;DR

VeREFINE combines hypothesis verification with physics-guided iterative refinement to improve object pose estimation accuracy and robustness in complex scenes, demonstrating significant improvements in datasets and real-world grasping tasks.

Contribution

This work introduces VeREFINE, a novel integrated approach that unifies pose verification and physics-based refinement for enhanced object pose estimation.

Findings

Improves pose estimation accuracy across multiple datasets.

Enhances grasp success rate in real-world experiments.

Compatible with various pose estimators and refiners.

Abstract

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.