MemoryScan: Smart Digital Transformation of Large-scale Environments for Eliciting Location Specific Knowledge

MemoryScan is a prototype community-scale virtual environment platform to elicit memory recall from current and former citizens and visitors of a selected municipality. Gathering recollections on a continuous community scale enables location-based stories to be visually tied together rather than exi...

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Vydáno v:IEEE International Symposium on Mixed and Augmented Reality Workshops (Online) s. 207 - 211
Hlavní autoři: Michlowitz, Robert A., Kider, Joseph T., Walters, Lori C.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2022
Témata:
Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual Reality
Human-centered computing-Interaction design-Systems and tools for interaction design
Point cloud compression
Prototypes
Resists
Solid modeling
Three-dimensional displays
Urban areas
Virtual environments
ISSN:2771-1110
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Shrnutí:MemoryScan is a prototype community-scale virtual environment platform to elicit memory recall from current and former citizens and visitors of a selected municipality. Gathering recollections on a continuous community scale enables location-based stories to be visually tied together rather than existing as locationally independent reflections. The interdisciplinary project team will examine the potential advantages of using a spatially realistic virtual environment to assist in the capture of participant reflections and collect supporting privately held documents. Designed as a large 3D photorealistic environment, MemoryScan will use an eXtended reality (XR) Head-Mounted Display (HMD) or PC interface to permit the capture of oral reflections of a community's heritage with remote participant access. Should this project's experimental testing validate this view, the fully developed system will be made available to crowdsource information on a national scale. This system would benefit historians, anthropologists, political scientists, city planners, and others.
ISSN:2771-1110
DOI:10.1109/ISMAR-Adjunct57072.2022.00046