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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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