BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
<|reference_start|>BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects: 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/.<|reference_end|>
@article{hodan2024bop,
    title={BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of
  Seen and Unseen Rigid Objects},
    author={Tomas Hodan, Martin Sundermeyer, Yann Labbe, Van Nguyen Nguyen, Gu
  Wang, Eric Brachmann, Bertram Drost, Vincent Lepetit, Carsten Rother, Jiri
  Matas},
    journal={arXiv preprint arXiv:2403.09799},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09799},
    primaryClass={cs.CV cs.RO}
}