Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation

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.