用物理约束优化6D物体位姿，让估计结果更符合现实。
PhysPose: Refining 6D Object Poses with Physical Constraints
- 后处理优化引入不穿透和重力约束，提升位姿合理性。
- 在YCB-Video上达到最新最佳准确率，HOPE-Video上也显著提升。
- 适合需要真实物理一致性的机器人抓取等应用。
从图像中精确估计6D物体位姿是物体中心场景理解的关键问题，广泛应用于机器人、增强现实和场景重建。尽管近期进展显著，现有方法常产生物理上不一致的位姿估计，限制了其在真实场景中的部署。本文提出PhysPose，一种通过后处理优化集成物理推理的新方法，强制执行非穿透和重力约束。利用场景几何信息，PhysPose对位姿估计进行精炼，确保物理合理性。该方法在BOP基准的YCB-Video数据集上达到最新最佳性能，并在HOPE-Video数据集上优于现有方法。此外，我们在机器人任务中验证了其效果，显著提升了复杂抓放任务的成功率，凸显了物理一致性在实际应用中的重要性。
原文摘要 · Abstract (English)
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.
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