  We present the evaluation methodology, datasets and results of the BOP
Challenge 2023, the fifth in a series of public competitions organized to
capture the state of the art in model-based 6D object pose estimation from an
RGB/RGB-D image and related tasks. Besides the three tasks from 2022
(model-based 2D detection, 2D segmentation, and 6D localization of objects seen
during training), the 2023 challenge introduced new variants of these tasks
focused on objects unseen during training. In the new tasks, methods were
required to learn new objects during a short onboarding stage (max 5 minutes, 1
GPU) from provided 3D object models. The best 2023 method for 6D localization
of unseen objects (GenFlow) notably reached the accuracy of the best 2020
method for seen objects (CosyPose), although being noticeably slower. The best
2023 method for seen objects (GPose) achieved a moderate accuracy improvement
but a significant 43% run-time improvement compared to the best 2022
counterpart (GDRNPP). Since 2017, the accuracy of 6D localization of seen
objects has improved by more than 50% (from 56.9 to 85.6 AR_C). The online
evaluation system stays open and is available at: http://bop.felk.cvut.cz/.
