BOP Challenge 2020 on 6D Object Localization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342591" target="_blank" >RIV/68407700:21230/20:00342591 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-66096-3_39" target="_blank" >https://doi.org/10.1007/978-3-030-66096-3_39</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-66096-3_39" target="_blank" >10.1007/978-3-030-66096-3_39</a>
Alternative languages
Result language
angličtina
Original language name
BOP Challenge 2020 on 6D Object Localization
Original language description
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image. In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic trainining images generated by BlenderProc4BOP, a new open-source and light-weight physically-based renderer (PBR) and procedural data generator. Methods based on deep neural networks have finally caught up with methods based on point pair features, which were dominating previous editions of the challenge. Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time -- out of 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth was trained on PBR images only. Strong data augmentation was identified as a key component of the top-performing CosyPose method, and the photorealism of PBR images was demonstrated effective despite the augmentation. The online evaluation system stays open and is available at the project website: 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
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision – ECCV 2020 Workshops, Part II
ISBN
978-3-030-66095-6
ISSN
0302-9743
e-ISSN
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Number of pages
18
Pages from-to
577-594
Publisher name
Springer
Place of publication
Basel
Event location
Glasgow
Event date
Aug 23, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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