MessyTable: Instance Association in Multiple Camera Views

Zhongang Cai; Junzhe Zhang; Daxuan Ren; Cunjun Yu; Haiyu Zhao; Shuai Yi; Chai Kiat Yeo; Chen Change Loy

doi:10.1007/978-3-030-58621-8_1

MessyTable: Instance Association in Multiple Camera Views

Zhongang Cai, Junzhe Zhang, Daxuan Ren, Cunjun Yu, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Chen Change Loy^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. (Project page: https://caizhongang.github.io/projects/MessyTable/.).

Original language	English
Title of host publication	Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
Editors	Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1-16
Number of pages	16
ISBN (Print)	9783030586201
DOIs	https://doi.org/10.1007/978-3-030-58621-8_1
Publication status	Published - 2020
Externally published	Yes
Event	16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: Aug 23 2020 → Aug 28 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12356 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	8/23/20 → 8/28/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-58621-8_1

Cite this

Cai, Z., Zhang, J., Ren, D., Yu, C., Zhao, H., Yi, S., Yeo, C. K., & Change Loy, C. (2020). MessyTable: Instance Association in Multiple Camera Views. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings (pp. 1-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12356 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58621-8_1

Cai, Zhongang ; Zhang, Junzhe ; Ren, Daxuan et al. / MessyTable : Instance Association in Multiple Camera Views. Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. editor / Andrea Vedaldi ; Horst Bischof ; Thomas Brox ; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. pp. 1-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9622b3c0051445b082528e14a9540f16,

title = "MessyTable: Instance Association in Multiple Camera Views",

abstract = "We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. (Project page: https://caizhongang.github.io/projects/MessyTable/.).",

author = "Zhongang Cai and Junzhe Zhang and Daxuan Ren and Cunjun Yu and Haiyu Zhao and Shuai Yi and Yeo, \{Chai Kiat\} and \{Change Loy\}, Chen",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-58621-8\_1",

language = "English",

isbn = "9783030586201",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "1--16",

editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",

booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings",

address = "Germany",

}

Cai, Z, Zhang, J, Ren, D, Yu, C, Zhao, H, Yi, S, Yeo, CK & Change Loy, C 2020, MessyTable: Instance Association in Multiple Camera Views. in A Vedaldi, H Bischof, T Brox & J-M Frahm (eds), Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12356 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 1-16, 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 8/23/20. https://doi.org/10.1007/978-3-030-58621-8_1

MessyTable: Instance Association in Multiple Camera Views. / Cai, Zhongang; Zhang, Junzhe; Ren, Daxuan et al.
Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. ed. / Andrea Vedaldi; Horst Bischof; Thomas Brox; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. p. 1-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12356 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - MessyTable

T2 - 16th European Conference on Computer Vision, ECCV 2020

AU - Cai, Zhongang

AU - Zhang, Junzhe

AU - Ren, Daxuan

AU - Yu, Cunjun

AU - Zhao, Haiyu

AU - Yi, Shuai

AU - Yeo, Chai Kiat

AU - Change Loy, Chen

PY - 2020

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N2 - We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. (Project page: https://caizhongang.github.io/projects/MessyTable/.).

AB - We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. (Project page: https://caizhongang.github.io/projects/MessyTable/.).

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SN - 9783030586201

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

Y2 - 23 August 2020 through 28 August 2020

ER -

Cai Z, Zhang J, Ren D, Yu C, Zhao H, Yi S et al. MessyTable: Instance Association in Multiple Camera Views. In Vedaldi A, Bischof H, Brox T, Frahm JM, editors, Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 1-16. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-58621-8_1

MessyTable: Instance Association in Multiple Camera Views

Abstract

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Conference

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

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