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Conference Papers Year : 2020

Towards an Understanding of Augmented Reality Extensions for Existing 3D Data Analysis Tools

Xiyao Wang
Lonni Besançon
Mickael Sereno
Mehdi Ammi
Tobias Isenberg

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.
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hal-02442690 , version 1 (16-01-2020)

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Xiyao Wang, David Rousseau, Lonni Besançon, Mickael Sereno, Mehdi Ammi, et al.. Towards an Understanding of Augmented Reality Extensions for Existing 3D Data Analysis Tools. CHI '20 - 38th SIGCHI conference on Human Factors in computing systems, Apr 2020, Honolulu, United States. ⟨10.1145/3313831.3376657⟩. ⟨hal-02442690⟩
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