# Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors

arXiv: https://arxiv.org/abs/2005.06262 · HTML: https://mlsanity.com/paper/2005.06262/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 3 of 3)

- [ZePHyR: Zero-shot Pose Hypothesis Rating](https://mlsanity.com/paper/2104.13526/) (2021)
- [On the Tightness of Semidefinite Relaxations for Rotation Estimation](https://mlsanity.com/paper/2101.02099/) (2021)
- [DeepRM: Deep Recurrent Matching for 6D Pose Refinement](https://mlsanity.com/paper/2205.14474/) (2022)

## Abstract (arXiv)

In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.