Alexander Gall
, Anja Heim, Eduard Gröller, Christoph Heinzl
MARV: Multiview Augmented Reality Visualisation for Exploring Rich Material Data
Computer Graphics Forum, 44(6), September 2025.
Information
- Publication Type: Journal Paper (without talk)
- Workgroup(s)/Project(s):
- Date: September 2025
- Article Number: e70150
- DOI: 10.1111/cgf.70150
- ISSN: 1467-8659
- Journal: Computer Graphics Forum
- Number: 6
- Pages: 15
- Volume: 44
- Publisher: WILEY
- Keywords: virtual environments, augmented reality, immersive analytics, scientific visualisation, visualization, visual analytics
Abstract
Rich material data is complex, large and heterogeneous, integrating primary and secondary non-destructive testing data for spatial, spatio-temporal, as well as high-dimensional data analyses. Currently, materials experts mainly rely on conventional desktop-based systems using 2D visualisation techniques, which render respective analyses a time-consuming and mentally demanding challenge. MARV is a novel immersive visual analytics system, which makes analyses of such data more effective and engaging in an augmented reality setting. For this purpose, MARV includes three newly designed visualisation techniques: MDD Glyphs with a Skewness Kurtosis Mapper, Temporal Evolution Tracker, and Chrono Bins, facilitating interactive exploration and comparison of multidimensional distributions of attribute data from multiple time steps. A qualitative evaluation conducted with materials experts in a real-world case study demonstrates the benefits of the proposed visualisation techniques. This evaluation revealed that combining spatial and abstract data in an immersive environment improves their analytical capabilities and facilitates the identification of patterns, anomalies, as well as changes over time.Additional Files and Images
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Weblinks
BibTeX
@article{gall-2025-marv,
title = "MARV: Multiview Augmented Reality Visualisation for
Exploring Rich Material Data",
author = "Alexander Gall and Anja Heim and Eduard Gr\"{o}ller and
Christoph Heinzl",
year = "2025",
abstract = "Rich material data is complex, large and heterogeneous,
integrating primary and secondary non-destructive testing
data for spatial, spatio-temporal, as well as
high-dimensional data analyses. Currently, materials experts
mainly rely on conventional desktop-based systems using 2D
visualisation techniques, which render respective analyses a
time-consuming and mentally demanding challenge. MARV is a
novel immersive visual analytics system, which makes
analyses of such data more effective and engaging in an
augmented reality setting. For this purpose, MARV includes
three newly designed visualisation techniques: MDD Glyphs
with a Skewness Kurtosis Mapper, Temporal Evolution Tracker,
and Chrono Bins, facilitating interactive exploration and
comparison of multidimensional distributions of attribute
data from multiple time steps. A qualitative evaluation
conducted with materials experts in a real-world case study
demonstrates the benefits of the proposed visualisation
techniques. This evaluation revealed that combining spatial
and abstract data in an immersive environment improves their
analytical capabilities and facilitates the identification
of patterns, anomalies, as well as changes over time.",
month = sep,
articleno = "e70150",
doi = "10.1111/cgf.70150",
issn = "1467-8659",
journal = "Computer Graphics Forum",
number = "6",
pages = "15",
volume = "44",
publisher = "WILEY",
keywords = "virtual environments, augmented reality, immersive
analytics, scientific visualisation, visualization, visual
analytics",
URL = "https://www.cg.tuwien.ac.at/research/publications/2025/gall-2025-marv/",
}