# REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination

arXiv: https://arxiv.org/abs/2010.12807 · HTML: https://mlsanity.com/paper/2010.12807/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework](https://mlsanity.com/paper/2204.01080/) (2022)
- [A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration](https://mlsanity.com/paper/2410.10295/) (2024)
- [SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios](https://mlsanity.com/paper/2403.09317/) (2024)
- [Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation](https://mlsanity.com/paper/2109.12266/) (2021)
- [Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy](https://mlsanity.com/paper/2207.00260/) (2022)
- [Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction](https://mlsanity.com/paper/2308.08518/) (2023)
- [DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates](https://mlsanity.com/paper/2504.07335/) (2025)

## Abstract (arXiv)

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.