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EPOS: Estimating 6D Pose of Objects with Symmetries

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342587" target="_blank" >RIV/68407700:21230/20:00342587 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/CVPR42600.2020.01172" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.01172</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR42600.2020.01172" target="_blank" >10.1109/CVPR42600.2020.01172</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    EPOS: Estimating 6D Pose of Objects with Symmetries

  • Original language description

    We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk.cvut.cz/epos.

  • 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

    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition

  • ISBN

    978-1-7281-7169-2

  • ISSN

    1063-6919

  • e-ISSN

    2575-7075

  • Number of pages

    10

  • Pages from-to

    11700-11709

  • Publisher name

    IEEE Computer Society

  • Place of publication

    USA

  • Event location

    Seattle

  • Event date

    Jun 13, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article