Alexander Gall, Meister Eduard Gröller, Christoph Heinzl
ImNDT: Immersive Workspace for the Analysis of Multidimensional Material Data From Non-Destructive Testing
In Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology, pages 1-11. December 2021.
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Information

  • Publication Type: Conference Paper
  • Workgroup(s)/Project(s):
  • Date: December 2021
  • Booktitle: Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology
  • Call for Papers: Call for Paper
  • DOI: 10.1145/3489849.3489851
  • Event: 27th ACM Symposium on Virtual Reality Software and Technology
  • Open Access: yes
  • Pages (from): 1
  • Pages (to): 11

Abstract

An analysis of large multidimensional volumetric data as generated by non-destructive testing (NDT) techniques, e.g., X-ray computed tomography (XCT), can hardly be evaluated using standard 2D visualization techniques on desktop monitors. The analysis of fiber-reinforced polymers (FRPs) is currently a time-consuming and cognitively demanding task, as FRPs have a complex spatial structure, consisting of several hundred thousand fibers, each having more than twenty different extracted features. This paper presents ImNDT, a novel visualization system, which offers material experts an immersive exploration of multidimensional secondary data of FRPs. Our system is based on a virtual reality (VR) head-mounted device (HMD) to enable fluid and natural explorations through embodied navigation, the avoidance of menus, and manual mode switching. We developed immersive visualization and interaction methods tailored to the characterization of FRPs, such as a Model in Miniature, a similarity network, and a histo-book. An evaluation of our techniques with domain experts showed advantages in discovering structural patterns and similarities. Especially novices can strongly benefit from our intuitive representation and spatial rendering.

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BibTeX

@inproceedings{Gall2021,
  title =      "ImNDT: Immersive Workspace for the Analysis of
               Multidimensional Material Data From Non-Destructive Testing",
  author =     "Alexander Gall and Meister Eduard Gr\"{o}ller and Christoph
               Heinzl",
  year =       "2021",
  abstract =   "An analysis of large multidimensional volumetric data as
               generated by non-destructive testing (NDT) techniques, e.g.,
               X-ray computed tomography (XCT), can hardly be evaluated
               using standard 2D visualization techniques on desktop
               monitors. The analysis of fiber-reinforced polymers (FRPs)
               is currently a time-consuming and cognitively demanding
               task, as FRPs have a complex spatial structure, consisting
               of several hundred thousand fibers, each having more than
               twenty different extracted features. This paper presents
               ImNDT, a novel visualization system, which offers material
               experts an immersive exploration of multidimensional
               secondary data of FRPs. Our system is based on a virtual
               reality (VR) head-mounted device (HMD) to enable fluid and
               natural explorations through embodied navigation, the
               avoidance of menus, and manual mode switching. We developed
               immersive visualization and interaction methods tailored to
               the characterization of FRPs, such as a Model in Miniature,
               a similarity network, and a histo-book. An evaluation of our
               techniques with domain experts showed advantages in
               discovering structural patterns and similarities. Especially
               novices can strongly benefit from our intuitive
               representation and spatial rendering.",
  month =      dec,
  booktitle =  "Proceedings of the 27th ACM Symposium on Virtual Reality
               Software and Technology",
  doi =        "10.1145/3489849.3489851",
  event =      "27th ACM Symposium on Virtual Reality Software and
               Technology",
  pages =      "1--11",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Gall2021/",
}