Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress
Antarctic Heritage presents unrivalled opportunities for contemporary computational visualizationtechniques. These range from compelling immersive heritage experiences for the general public,through to the more exacting development of accurate digital archives for scholarly use. Game engines have a...
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ftunivtasecite:oai:ecite.utas.edu.au:143898 2023-05-15T13:59:46+02:00 Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress Morse, P Staal, T Reading, A 2020 application/pdf http://ecite.utas.edu.au/143898 en eng Scientific Committee on Antarctic Research (SCAR) http://ecite.utas.edu.au/143898/1/143898-Game engines, photogrammetry and deep learning.pdf Morse, P and Staal, T and Reading, A, Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress, Scientific Committee on Antarctic Research (SCAR) Open Science Conference 2020: Antarctic - Global Connections, 3-7 August 2020, Online, pp. 1-10. (2020) [Non Refereed Conference Paper] http://ecite.utas.edu.au/143898 Creative Arts and Writing Visual arts Visual arts not elsewhere classified Non Refereed Conference Paper NonPeerReviewed 2020 ftunivtasecite 2021-06-21T22:16:28Z Antarctic Heritage presents unrivalled opportunities for contemporary computational visualizationtechniques. These range from compelling immersive heritage experiences for the general public,through to the more exacting development of accurate digital archives for scholarly use. Game engines have a wide variety of heritage applications as development environments forcomputational humanities, digital museology and GLAM-sector applications. Reconstruction ofhistoric Antarctic sites using satellite and other geophysical data in concert with photogrammetricscene reconstruction enable the construction of physically accurate heritage site models. These canbe displayed as immersive screen experiences (e.g. VR, Augmented Reality and Domeenvironments) and afford novel visual analytics approaches to Antarctic heritage data. Associatedhistorical textual, map, photographic and film materials can be restored, animated, translated into3D scenes and actors, and colourised using machine learning techniques (Deep Learning)employed in the film, special effects and games industries. Immersive interactive simulations that embed historic materials demonstrate new ways of interactingwith museum collections and scientific archives, new digital methodologies of historical scholarshipand effective ways of exposing fragile archival materials for general and specialist audiences.Interactive post-cinematic narratives suggest novel opportunities for dramatising the experience ofsignificant artefacts, bringing place, biography, history and science alive. Remote environments,both in space and time, become far more accessible and available to contemporary enquiry. A demonstration model of the Mawsons Huts Historic Site will be presented, using a computergame engine. Conference Object Antarc* Antarctic eCite UTAS (University of Tasmania) Antarctic |
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Creative Arts and Writing Visual arts Visual arts not elsewhere classified |
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Creative Arts and Writing Visual arts Visual arts not elsewhere classified Morse, P Staal, T Reading, A Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress |
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Creative Arts and Writing Visual arts Visual arts not elsewhere classified |
description |
Antarctic Heritage presents unrivalled opportunities for contemporary computational visualizationtechniques. These range from compelling immersive heritage experiences for the general public,through to the more exacting development of accurate digital archives for scholarly use. Game engines have a wide variety of heritage applications as development environments forcomputational humanities, digital museology and GLAM-sector applications. Reconstruction ofhistoric Antarctic sites using satellite and other geophysical data in concert with photogrammetricscene reconstruction enable the construction of physically accurate heritage site models. These canbe displayed as immersive screen experiences (e.g. VR, Augmented Reality and Domeenvironments) and afford novel visual analytics approaches to Antarctic heritage data. Associatedhistorical textual, map, photographic and film materials can be restored, animated, translated into3D scenes and actors, and colourised using machine learning techniques (Deep Learning)employed in the film, special effects and games industries. Immersive interactive simulations that embed historic materials demonstrate new ways of interactingwith museum collections and scientific archives, new digital methodologies of historical scholarshipand effective ways of exposing fragile archival materials for general and specialist audiences.Interactive post-cinematic narratives suggest novel opportunities for dramatising the experience ofsignificant artefacts, bringing place, biography, history and science alive. Remote environments,both in space and time, become far more accessible and available to contemporary enquiry. A demonstration model of the Mawsons Huts Historic Site will be presented, using a computergame engine. |
format |
Conference Object |
author |
Morse, P Staal, T Reading, A |
author_facet |
Morse, P Staal, T Reading, A |
author_sort |
Morse, P |
title |
Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress |
title_short |
Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress |
title_full |
Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress |
title_fullStr |
Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress |
title_full_unstemmed |
Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress |
title_sort |
game engines, photogrammetry and deep learning for antarctic heritage visualization: 2020 work-in-progress |
publisher |
Scientific Committee on Antarctic Research (SCAR) |
publishDate |
2020 |
url |
http://ecite.utas.edu.au/143898 |
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Antarctic |
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Antarctic |
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Antarc* Antarctic |
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Antarc* Antarctic |
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http://ecite.utas.edu.au/143898/1/143898-Game engines, photogrammetry and deep learning.pdf Morse, P and Staal, T and Reading, A, Game engines, photogrammetry and deep learning for Antarctic heritage visualization: 2020 Work-in-progress, Scientific Committee on Antarctic Research (SCAR) Open Science Conference 2020: Antarctic - Global Connections, 3-7 August 2020, Online, pp. 1-10. (2020) [Non Refereed Conference Paper] http://ecite.utas.edu.au/143898 |
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