  Object 6D pose estimation is a fundamental task in many applications.
Conventional methods solve the task by detecting and matching the keypoints,
then estimating the pose. Recent efforts bringing deep learning into the
problem mainly overcome the vulnerability of conventional methods to
environmental variation due to the hand-crafted feature design. However, these
methods cannot achieve end-to-end learning and good interpretability at the
same time. In this paper, we propose REDE, a novel end-to-end object pose
estimator using RGB-D data, which utilizes network for keypoint regression, and
a differentiable geometric pose estimator for pose error back-propagation.
Besides, to achieve better robustness when outlier keypoint prediction occurs,
we further propose a differentiable outliers elimination method that regresses
the candidate result and the confidence simultaneously. Via confidence weighted
aggregation of multiple candidates, we can reduce the effect from the outliers
in the final estimation. Finally, following the conventional method, we apply a
learnable refinement process to further improve the estimation. The
experimental results on three benchmark datasets show that REDE slightly
outperforms the state-of-the-art approaches and is more robust to object
occlusion.
