CosyPose: Consistent Multi-view Multi-object 6D Pose Estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00347779" target="_blank" >RIV/68407700:21730/20:00347779 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-58520-4_34" target="_blank" >https://doi.org/10.1007/978-3-030-58520-4_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58520-4_34" target="_blank" >10.1007/978-3-030-58520-4_34</a>
Alternative languages
Result language
angličtina
Original language name
CosyPose: Consistent Multi-view Multi-object 6D Pose Estimation
Original language description
We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a single-view single-object 6D pose estimation method, which we use to generate 6D object pose hypotheses. Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all objects in a single consistent scene. Our approach explicitly handles object symmetries, does not require depth measurements, is robust to missing or incorrect object hypotheses, and automatically recovers the number of objects in the scene. Third, we develop a method for global scene refinement given multiple object hypotheses and their correspondences across views. This is achieved by solving an object-level bundle adjustment problem that refines the poses of cameras and objects to minimize the reprojection error in all views. We demonstrate that the proposed method, dubbed CosyPose, outperforms current state-of-the-art results for single-view and multi-view 6D object pose estimation by a large margin on two challenging benchmarks: the YCB-Video and T-LESS datasets. Code and pre-trained models are available on the project webpage.
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/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII
ISBN
978-3-030-58519-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
18
Pages from-to
574-591
Publisher name
Springer
Place of publication
Cham
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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