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Book Chapter

A Framework for Realistic Virtual Representation for Immersive Training Environments.

  • Caolan Plumb
  • Farzad Pour Rahimian
  • Diptangshu Pandit
  • Hannah Thomas
  • Nigel Clark

As mixed-reality (XR) technology becomes more available, virtually simulated training scenarios have shown great potential in enhancing training effectiveness. Realistic virtual representation plays a crucial role in creating immersive experiences that closely mimic real-world scenarios. With reference to previous methodological developments in the creation of information-rich digital reconstructions, this paper proposes a framework encompassing key components of the 3D scanning pipeline. While 3D scanning techniques have advanced significantly, several challenges persist in the field. These challenges include data acquisition, noise reduction, mesh and texture optimisation, and separation of components for independent interaction. These complexities necessitate the search for an optimised framework that addresses these challenges and provides practical solutions for creating realistic virtual representations in immersive training environments. The following exploration acknowledges and addresses challenges presented by the photogrammetry and laser-scanning pipeline, seeking to prepare scanned assets for real-time virtual simulation in a games-engine. This methodology employs both a camera and handheld laser-scanner for accurate data acquisition. Reality Capture is used to combine the geometric data and surface detail of the equipment. To clean the scanned asset, Blender is used for mesh retopology and reprojection of scanned textures, and attention given to correct lighting details and normal mapping, thus preparing the equipment to be interacted with by Virtual Reality (VR) users within Unreal Engine. By combining these elements, the proposed framework enables realistic representation of industrial equipment for the creation of training scenarios that closely resemble real-world contexts

  • Keywords:
  • Digital twin; 3D reconstruction; Virtual reality; Laser scanning; Photogrammetry; Training simulation; Unreal Engine,
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Caolan Plumb

Teesside University, United Kingdom

Farzad Pour Rahimian

Teesside University, United Kingdom - ORCID: 0000-0001-7443-4723

Diptangshu Pandit

Teesside University, United Kingdom - ORCID: 0000-0001-7647-3443

Hannah Thomas

The Faraday Centre LTD, United Kingdom

Nigel Clark

The Faraday Centre LTD, United Kingdom

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  • Publication Year: 2023
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  • Publication Year: 2023

Chapter Information

Chapter Title

A Framework for Realistic Virtual Representation for Immersive Training Environments.

Authors

Caolan Plumb, Farzad Pour Rahimian, Diptangshu Pandit, Hannah Thomas, Nigel Clark

DOI

10.36253/979-12-215-0289-3.26

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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