Towards an understanding of augmented reality extensions for existing 3D data analysis tools

Xiyao Wang; Lonni Besançon; David Rousseau; Mickael Sereno; Mehdi Ammi; Tobias Isenberg

doi:10.1145/3313831.3376657

Towards an understanding of augmented reality extensions for existing 3D data analysis tools

Xiyao Wang, Lonni Besançon, David Rousseau, Mickael Sereno, Mehdi Ammi, Tobias Isenberg

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

79 Citations (Scopus)

Abstract

We present an observational study with domain experts to understand how augmented reality (AR) extensions to traditional PC-based data analysis tools can help particle physicists to explore and understand 3D data. Our goal is to allow researchers to integrate stereoscopic AR-based visual representations and interaction techniques into their tools, and thus ultimately to increase the adoption of modern immersive analytics techniques in existing data analysis workflows. We use Microsoft's HoloLens as a lightweight and easily maintainable AR headset and replicate existing visualization and interaction capabilities on both the PC and the AR view. We treat the AR headset as a second yet stereoscopic screen, allowing researchers to study their data in a connected multi-view manner. Our results indicate that our collaborating physicists appreciate a hybrid data exploration setup with an interactive AR extension to improve their understanding of particle collision events.

Original language	English
Title of host publication	CHI'20 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Editors	Joanna McGrenere, Andy Cockburn
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	1-13
Number of pages	14
ISBN (Electronic)	9781450367080
DOIs	https://doi.org/10.1145/3313831.3376657
Publication status	Published - 2020
Externally published	Yes
Event	International Conference on Human Factors in Computing Systems 2020 - Hawaii, Honolulu , United States of America Duration: 25 Apr 2020 → 30 Apr 2020 Conference number: 38th https://chi2020.acm.org (Website) https://dl.acm.org/doi/proceedings/10.1145/3313831 (Proceedings)

Conference

Conference	International Conference on Human Factors in Computing Systems 2020
Abbreviated title	CHI 2020
Country/Territory	United States of America
City	Honolulu
Period	25/04/20 → 30/04/20
Internet address	https://chi2020.acm.org (Website) https://dl.acm.org/doi/proceedings/10.1145/3313831 (Proceedings)

Keywords

3D visualization
hybrid visualization system
immersive analytics
user interface

Access to Document

10.1145/3313831.3376657

Cite this

Wang, X., Besançon, L., Rousseau, D., Sereno, M., Ammi, M., & Isenberg, T. (2020). Towards an understanding of augmented reality extensions for existing 3D data analysis tools. In J. McGrenere, & A. Cockburn (Eds.), CHI'20 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13). Article 3376657 Association for Computing Machinery (ACM). https://doi.org/10.1145/3313831.3376657

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title = "Towards an understanding of augmented reality extensions for existing 3D data analysis tools",

abstract = "We present an observational study with domain experts to understand how augmented reality (AR) extensions to traditional PC-based data analysis tools can help particle physicists to explore and understand 3D data. Our goal is to allow researchers to integrate stereoscopic AR-based visual representations and interaction techniques into their tools, and thus ultimately to increase the adoption of modern immersive analytics techniques in existing data analysis workflows. We use Microsoft's HoloLens as a lightweight and easily maintainable AR headset and replicate existing visualization and interaction capabilities on both the PC and the AR view. We treat the AR headset as a second yet stereoscopic screen, allowing researchers to study their data in a connected multi-view manner. Our results indicate that our collaborating physicists appreciate a hybrid data exploration setup with an interactive AR extension to improve their understanding of particle collision events.",

keywords = "3D visualization, hybrid visualization system, immersive analytics, user interface",

author = "Xiyao Wang and Lonni Besan{\c c}on and David Rousseau and Mickael Sereno and Mehdi Ammi and Tobias Isenberg",

note = "Publisher Copyright: {\textcopyright} 2020 ACM. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Conference on Human Factors in Computing Systems 2020, CHI 2020 ; Conference date: 25-04-2020 Through 30-04-2020",

year = "2020",

doi = "10.1145/3313831.3376657",

language = "English",

pages = "1--13",

editor = "McGrenere, \{Joanna \} and Cockburn, \{Andy \}",

booktitle = "CHI'20 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://chi2020.acm.org, https://dl.acm.org/doi/proceedings/10.1145/3313831",

}

Wang, X, Besançon, L, Rousseau, D, Sereno, M, Ammi, M & Isenberg, T 2020, Towards an understanding of augmented reality extensions for existing 3D data analysis tools. in J McGrenere & A Cockburn (eds), CHI'20 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems., 3376657, Association for Computing Machinery (ACM), New York NY USA, pp. 1-13, International Conference on Human Factors in Computing Systems 2020, Honolulu , Hawaii, United States of America, 25/04/20. https://doi.org/10.1145/3313831.3376657

Towards an understanding of augmented reality extensions for existing 3D data analysis tools. / Wang, Xiyao; Besançon, Lonni; Rousseau, David et al.
CHI'20 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ed. / Joanna McGrenere; Andy Cockburn. New York NY USA: Association for Computing Machinery (ACM), 2020. p. 1-13 3376657.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Wang, Xiyao

AU - Besançon, Lonni

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AU - Sereno, Mickael

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AU - Isenberg, Tobias

N1 - Conference code: 38th

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AB - We present an observational study with domain experts to understand how augmented reality (AR) extensions to traditional PC-based data analysis tools can help particle physicists to explore and understand 3D data. Our goal is to allow researchers to integrate stereoscopic AR-based visual representations and interaction techniques into their tools, and thus ultimately to increase the adoption of modern immersive analytics techniques in existing data analysis workflows. We use Microsoft's HoloLens as a lightweight and easily maintainable AR headset and replicate existing visualization and interaction capabilities on both the PC and the AR view. We treat the AR headset as a second yet stereoscopic screen, allowing researchers to study their data in a connected multi-view manner. Our results indicate that our collaborating physicists appreciate a hybrid data exploration setup with an interactive AR extension to improve their understanding of particle collision events.

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KW - hybrid visualization system

KW - immersive analytics

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Wang X, Besançon L, Rousseau D, Sereno M, Ammi M, Isenberg T. Towards an understanding of augmented reality extensions for existing 3D data analysis tools. In McGrenere J, Cockburn A, editors, CHI'20 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York NY USA: Association for Computing Machinery (ACM). 2020. p. 1-13. 3376657 doi: 10.1145/3313831.3376657

Towards an understanding of augmented reality extensions for existing 3D data analysis tools

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