Game Engine-based Point Cloud Visualization and Perception for Situation Awareness of Crisis Indoor Environments

Zhenyu Liu, Runnan Fu, Linjun Wang, Yuzhen Jin, Theodoros Papakostas, Xenia Una Mainelli, Robert Voûte, Edward Verbree

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Because unknown interior layouts can have serious consequences in time-sensitive situations, crisis response teams request many potential solutions for visualizing indoor environments in crisis scenarios. This research uses a game engine to directly visualize point cloud data input of indoor environments for generating clear interaction between the environment and viewers, to aid decision-making in high-stress moments. The prospective final product is an integration of game-oriented visualization and cartography, hosted within Unreal Engine 4 (UE4), allowing users to navigate throughout an indoor environment, and customizing certain interaction features. The UE4 project consists of 4 modules: data preprocessing, render style, functional module, and user interface. Finally, this research uses a single-floor indoor point cloud dataset collected from a building in Rotterdam, the Netherlands for the implementation.
Original languageEnglish
Title of host publicationLBS 2021: Proceedings of the 16th International Conference on Location Based Services
EditorsAnahid Basiri, Georg Gartner Gartner, Haosheng Huang
PublisherTU Wien
Pages183-194
Number of pages12
DOIs
Publication statusPublished - 2021
EventLBS 2021: 16th International Conference on Location Based Services (Online) - Glasgow, United Kingdom
Duration: 24 Nov 202125 Nov 2021

Conference

ConferenceLBS 2021: 16th International Conference on Location Based Services (Online)
Country/TerritoryUnited Kingdom
CityGlasgow
Period24/11/2125/11/21

Keywords

  • Game Engine
  • Unreal Engine 4
  • 3D Visualization
  • Situation Awareness
  • Point Cloud
  • Indoor Environment
  • Crisis Scenario

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