BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00389857" target="_blank" >RIV/68407700:21230/24:00389857 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPRW63382.2024.00570" target="_blank" >https://doi.org/10.1109/CVPRW63382.2024.00570</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPRW63382.2024.00570" target="_blank" >10.1109/CVPRW63382.2024.00570</a>
Alternative languages
Result language
angličtina
Original language name
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
Original language description
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 (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 ARC). The online evaluation system stays open and is available at: bop.felk.cvut.cz.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISBN
979-8-3503-6547-4
ISSN
2160-7508
e-ISSN
2160-7516
Number of pages
10
Pages from-to
5610-5619
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Seattle
Event date
Jun 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001327781705083