An Efficient Workflow for Representing Real-world Urban Environments in Game Engines using Open-source Software and Data

Arash Shahbaz Badr, Raffaele De Amicis

2022

Abstract

Game engines (GEs) constitute a powerful platform for visualizing real geographies in immersive virtual space, and in the last two years, remarkable strides have been made by the leading providers of Geographic Information System (GIS) software and services, including Esri and Cesium, toward integrating their products in GEs. Notwithstanding the strengths of GEs, they lack support for many common GIS file formats, and there exist only limited georeferencing possibilities. Visualizing large-scale geolocations involves high authoring costs, and the shortcomings of GEs further complicate the workflow. In this paper, we present a workflow and its implementation for creating large immersive virtual environments that accurately represent real-world urban areas. The benefits of the presented development are threefold. First, it makes the process more efficient by automating multiple steps and incorporating a large portion of the workflow inside the GE. Second, it facilitates an interactive framework by allowing the developer to efficiently extend the scene components with functionalities and interactions. Third, it entirely relies on open-source software and data, making it suitable for many non-commercial domains. To showcase the effectiveness of the tool, we created a virtual replica of an actual city consisting of the terrain, the streets, and the buildings.

Paper Citation

in Harvard Style

Shahbaz Badr A. and De Amicis R. (2022). An Efficient Workflow for Representing Real-world Urban Environments in Game Engines using Open-source Software and Data. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 1: GRAPP; ISBN 978-989-758-555-5, SciTePress, pages 103-114. DOI: 10.5220/0010916900003124

in Bibtex Style

@conference{grapp22,
author={Arash Shahbaz Badr and Raffaele De Amicis},
title={An Efficient Workflow for Representing Real-world Urban Environments in Game Engines using Open-source Software and Data},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 1: GRAPP},
year={2022},
pages={103-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010916900003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 1: GRAPP
TI - An Efficient Workflow for Representing Real-world Urban Environments in Game Engines using Open-source Software and Data
SN - 978-989-758-555-5
AU - Shahbaz Badr A.
AU - De Amicis R.
PY - 2022
SP - 103
EP - 114
DO - 10.5220/0010916900003124
PB - SciTePress