The estimation of 6D object poses is a fundamental task in many computer vision applications. Particularly, in high risk scenarios such as human-robot interaction, industrial inspection, and automation, reliable pose estimates are crucial. In the last years, increasingly accurate and robust deep-learning-based approaches for 6D object pose estimation have been proposed. Many top-performing methods are not end-to-end trainable but consist of multiple stages. In the context of deep uncertainty quantification, deep ensembles are considered as state of the art since they have been proven to produce well-calibrated and robust uncertainty estimates. However, deep ensembles can only be applied to methods that can be trained end-to-end. In this work, we propose a method to quantify the uncertainty of multi-stage 6D object pose estimation approaches with deep ensembles. For the implementation, we choose SurfEmb as representative, since it is one of the top-performing 6D object pose estimation approaches in the BOP Challenge 2022. We apply established metrics and concepts for deep uncertainty quantification to evaluate the results. Furthermore, we propose a novel uncertainty calibration score for regression tasks to quantify the quality of the estimated uncertainty.

翻译：6D物体姿态估计是众多计算机视觉应用中的基础任务。特别是在人机交互、工业检测和自动化等高危场景中，可靠的姿态估计至关重要。近年来，基于深度学习的6D物体姿态估计方法日趋精准稳健。许多顶尖方法并非端到端可训练，而是由多个阶段组成。在深度不确定性量化研究中，深度集成因其能产生校准良好且鲁棒的不确定性估计而被视为前沿技术。然而，深度集成仅适用于可端到端训练的方法。本文提出一种利用深度集成量化多阶段6D物体姿态估计方法不确定性的方案。我们选用SurfEmb作为代表性方法进行实现，因其在BOP Challenge 2022中位列顶尖的6D物体姿态估计方法之一。我们采用深度不确定性量化中的成熟指标与概念进行结果评估，并针对回归任务提出一种新型不确定性校准评分，用以衡量估计不确定性的质量。