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  Learnable Online Graph Representations for 3D Multi-Object Tracking

Zaech, J.-N., Dai, D., Liniger, A., Danelljan, M., & Van Gool, L. (2022). Learnable Online Graph Representations for 3D Multi-Object Tracking. IEEE Robotics and Automation Letters. doi:10.1109/LRA.2022.3145952.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-444F-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-152F-B
Genre: Journal Article
Latex : Learnable Online Graph Representations for {3D} Multi-Object Tracking

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arXiv:2104.11747.pdf (Preprint), 3MB
 
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 Creators:
Zaech, Jan-Nico1, Author
Dai, Dengxin2, Author           
Liniger, Alexander1, Author
Danelljan, Martin1, Author
Van Gool, Luc1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              

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Language(s): eng - English
 Dates: 2021-04-232022
 Publication Status: Published online
 Pages: 13 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/LRA.2022.3145952
BibTex Citekey: Zaech2104.11747
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Title: IEEE Robotics and Automation Letters
Source Genre: Journal
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Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 2377-3766
CoNE: https://pure.mpg.de/cone/journals/resource/23773766