Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation

arXiv:2403.07741v2 Announce Type: replace
Abstract: 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 …

abstract applications arxiv automation computer computer vision cs.ai cs.cv fundamental human industrial object quantification risk robot robust type uncertainty vision