A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training. (arXiv:2302.06019v2 [cs.CV] UPDATED)

Real-world robotics applications demand object pose estimation methods that
work reliably across a variety of scenarios. Modern learning-based approaches
require large labeled datasets and tend to perform poorly outside the training
domain. Our first contribution is to develop a robust corrector module that
corrects pose estimates using depth information, thus enabling existing methods
to better generalize to new test domains; the corrector operates on semantic
keypoints (but is also applicable to other pose estimators) and is fully
differentiable. Our second …

applications arxiv datasets demand ensemble robotics self-training training work world