用不确定性指导知识蒸馏，让小模型更准更快地估物体重6自由度姿态。
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation
- 根据教师模型关键点的预测不确定性动态调整知识传递
- 在LINEMOD上实现比顶尖方法更优的小型模型性能
- 适合对实时性与精度都有要求的机器人、航天等场景
紧凑高效的6自由度物体姿态估计在机器人、增强现实和空间自主导航系统中至关重要，轻量级模型是实现实时精准性能的关键。本文提出一种面向基于关键点的6DoF姿态估计的新型不确定性感知端到端知识蒸馏框架。教师模型预测的关键点具有不同水平的不确定性，这些信息可在蒸馏过程中被利用以提升学生模型的准确性，同时保证其紧凑性。为此，我们设计了一种基于教师关键点预测不确定性的知识转移策略，通过调整对齐方式来优化学生与教师的预测匹配。此外，该方法利用不确定性感知的关键点对齐，在各自特征图的关键位置进行知识迁移。在广泛使用的LINEMOD基准上的实验表明，本方法在使用轻量级模型时实现了优于现有最先进方法的6DoF物体姿态估计性能。在SPEED+数据集上对航天器姿态估计的进一步验证，也展示了该方法在多种6DoF姿态估计场景下的鲁棒性。
原文摘要 · Abstract (English)
Compact and efficient 6DoF object pose estimation is crucial in applications such as robotics, augmented reality, and space autonomous navigation systems, where lightweight models are critical for real-time accurate performance. This paper introduces a novel uncertainty-aware end-to-end Knowledge Distillation (KD) framework focused on keypoint-based 6DoF pose estimation. Keypoints predicted by a large teacher model exhibit varying levels of uncertainty that can be exploited within the distillation process to enhance the accuracy of the student model while ensuring its compactness. To this end, we propose a distillation strategy that aligns the student and teacher predictions by adjusting the knowledge transfer based on the uncertainty associated with each teacher keypoint prediction. Additionally, the proposed KD leverages this uncertainty-aware alignment of keypoints to transfer the knowledge at key locations of their respective feature maps. Experiments on the widely-used LINEMOD benchmark demonstrate the effectiveness of our method, achieving superior 6DoF object pose estimation with lightweight models compared to state-of-the-art approaches. Further validation on the SPEED+ dataset for spacecraft pose estimation highlights the robustness of our approach under diverse 6DoF pose estimation scenarios.
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