Single Image Direct-Global Illumination Separation

Global light transport, including diffuse interreflections, caustic, refractions and subsurface scattering, is important to achieve photorealistic rendering. However rendering these phenomena is very time-consuming. Furthermore, many inverse rendering methods’ accuracy in computer graphics and compu...

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Main Author: Duan, Zhaoliang
Format: Others
Language:English
Published: W&M ScholarWorks 2020
Subjects:
Online Access:https://scholarworks.wm.edu/etd/1616444522
https://scholarworks.wm.edu/cgi/viewcontent.cgi?article=7111&context=etd
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spelling ndltd-wm.edu-oai-scholarworks.wm.edu-etd-71112021-09-18T05:32:05Z Single Image Direct-Global Illumination Separation Duan, Zhaoliang Global light transport, including diffuse interreflections, caustic, refractions and subsurface scattering, is important to achieve photorealistic rendering. However rendering these phenomena is very time-consuming. Furthermore, many inverse rendering methods’ accuracy in computer graphics and computer vision is adversely affected by the presence of global light transport. Therefore, separating direct-global light transport components is necessary to help in designing new rendering methods and in improving the accuracy of many image inverse methods. Prior work on separating direct and global light transport from photographs either requires expensive hardware, requires multiple photographs of the scene, or fails to accurately recover high frequency details. In this thesis, we propose two efficient and accurate single image direct global components separation methods. The first method is based on a sparse coding framework. We show good quality results on a variety of synthetic scenes. However, in practice, due to acquisition limitations, the sparse coding method is practically not feasible without accurate optical calibration that might be difficult to obtain. To address all different types of acquisition related artifacts introduced by the optical system, we introduce a second data-driven approach using a novel deep learning based method to automatically compensate for acquisition related artifacts. Our deep learning method achieves high quality decompositions on synthetic scenes as well as real scenes by capturing only a single photograph of the scene illuminated by a low-cost projector. Furthermore, we resolve the lighting frequency constraints of prior work, yielding more accurate decompositions for lighting frequency sensitive features such as subsurface scattering and specular light transport. 2020-01-01T08:00:00Z text application/pdf https://scholarworks.wm.edu/etd/1616444522 https://scholarworks.wm.edu/cgi/viewcontent.cgi?article=7111&context=etd © The Author http://creativecommons.org/licenses/by/4.0/ Dissertations, Theses, and Masters Projects English W&M ScholarWorks Computer Sciences
collection NDLTD
language English
format Others
sources NDLTD
topic Computer Sciences
spellingShingle Computer Sciences
Duan, Zhaoliang
Single Image Direct-Global Illumination Separation
description Global light transport, including diffuse interreflections, caustic, refractions and subsurface scattering, is important to achieve photorealistic rendering. However rendering these phenomena is very time-consuming. Furthermore, many inverse rendering methods’ accuracy in computer graphics and computer vision is adversely affected by the presence of global light transport. Therefore, separating direct-global light transport components is necessary to help in designing new rendering methods and in improving the accuracy of many image inverse methods. Prior work on separating direct and global light transport from photographs either requires expensive hardware, requires multiple photographs of the scene, or fails to accurately recover high frequency details. In this thesis, we propose two efficient and accurate single image direct global components separation methods. The first method is based on a sparse coding framework. We show good quality results on a variety of synthetic scenes. However, in practice, due to acquisition limitations, the sparse coding method is practically not feasible without accurate optical calibration that might be difficult to obtain. To address all different types of acquisition related artifacts introduced by the optical system, we introduce a second data-driven approach using a novel deep learning based method to automatically compensate for acquisition related artifacts. Our deep learning method achieves high quality decompositions on synthetic scenes as well as real scenes by capturing only a single photograph of the scene illuminated by a low-cost projector. Furthermore, we resolve the lighting frequency constraints of prior work, yielding more accurate decompositions for lighting frequency sensitive features such as subsurface scattering and specular light transport.
author Duan, Zhaoliang
author_facet Duan, Zhaoliang
author_sort Duan, Zhaoliang
title Single Image Direct-Global Illumination Separation
title_short Single Image Direct-Global Illumination Separation
title_full Single Image Direct-Global Illumination Separation
title_fullStr Single Image Direct-Global Illumination Separation
title_full_unstemmed Single Image Direct-Global Illumination Separation
title_sort single image direct-global illumination separation
publisher W&M ScholarWorks
publishDate 2020
url https://scholarworks.wm.edu/etd/1616444522
https://scholarworks.wm.edu/cgi/viewcontent.cgi?article=7111&context=etd
work_keys_str_mv AT duanzhaoliang singleimagedirectglobalilluminationseparation
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