  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.
