用概率分布预测6自由度姿态，提升单视角定位精度与多样性。
End-to-End Probabilistic Geometry-Guided Regression for 6DoF Object Pose Estimation
- 将传统单姿态预测改为输出姿态概率分布，增强不确定性建模。
- 在LM-O、YCB-V、ITODD数据集上优于现有方法，提升关键指标。
- 适合需要多候选姿态的XR、机器人抓取等高可靠性场景。
6D物体位姿估计是确定物体相对于选定坐标系的位置与朝向，是现代扩展现实（XR）应用的核心技术。当前最先进的6D位姿估计算法直接根据物体观测预测单一位姿。由于该问题本身存在病态性——多个不同位姿可能对应同一观测——为每次检测生成多个合理位姿估计具有重要价值。为此，我们重构了当前先进算法GDRNPP，提出端到端的概率几何引导回归方法EPRO-GDR。不同于仅输出单一位姿，本方法估计位姿的概率密度分布。基于BOP挑战赛定义的评估流程，我们在四个核心数据集上测试，结果表明：在LM-O、YCB-V和ITODD数据集上，EPRO-GDR性能优于现有方法。实验显示，通过预测姿态分布而非单一姿态，不仅能提升单视角位姿估计的精度，还能采样出多个有意义的位姿候选，增强系统鲁棒性。
原文摘要 · Abstract (English)
6D object pose estimation is the problem of identifying the position and orientation of an object relative to a chosen coordinate system, which is a core technology for modern XR applications. State-of-the-art 6D object pose estimators directly predict an object pose given an object observation. Due to the ill-posed nature of the pose estimation problem, where multiple different poses can correspond to a single observation, generating additional plausible estimates per observation can be valuable. To address this, we reformulate the state-of-the-art algorithm GDRNPP and introduce EPRO-GDR (End-to-End Probabilistic Geometry-Guided Regression). Instead of predicting a single pose per detection, we estimate a probability density distribution of the pose. Using the evaluation procedure defined by the BOP (Benchmark for 6D Object Pose Estimation) Challenge, we test our approach on four of its core datasets and demonstrate superior quantitative results for EPRO-GDR on LM-O, YCB-V, and ITODD. Our probabilistic solution shows that predicting a pose distribution instead of a single pose can improve state-of-the-art single-view pose estimation while providing the additional benefit of being able to sample multiple meaningful pose candidates.
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