Indoor scene generation method using radiance fields and super-resolution
Indoor scene generation in the digital realm has garnered significant attention within the computer vision domain, offering diverse applications ranging from architectural visualization to virtual reality experiences and gaming environments. Traditional methods relying on manual 3D modeling are time...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1753092024-04-26T15:43:39Z Indoor scene generation method using radiance fields and super-resolution Yang, Yida Liu Ziwei School of Computer Science and Engineering ziwei.liu@ntu.edu.sg Computer and Information Science Indoor scene generation Radiance fields Super-resolution Generative adversarial network Indoor scene generation in the digital realm has garnered significant attention within the computer vision domain, offering diverse applications ranging from architectural visualization to virtual reality experiences and gaming environments. Traditional methods relying on manual 3D modeling are time-consuming and lack scalability. Recent advancements, particularly the introduction of Neural Radiance Field (NeRF), have shown promise in representing indoor scenes comprehensively and synthesizing novel views. This Final Year Project (FYP) proposes a method combining NeRF-based scene generation with single image super-resolution methods. By leveraging a generative adversarial network (GAN) with radiance field and employing convolutional neural networks (CNNs) for super-resolution, the method aims to enhance the realism and resolution of generated indoor scenes. Experimental results demonstrate improvements over baseline models, although issues regarding consistency are noted and discussed. Bachelor's degree 2024-04-22T09:20:34Z 2024-04-22T09:20:34Z 2024 Final Year Project (FYP) Yang, Y. (2024). Indoor scene generation method using radiance fields and super-resolution. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175309 https://hdl.handle.net/10356/175309 en application/pdf Nanyang Technological University |
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Computer and Information Science Indoor scene generation Radiance fields Super-resolution Generative adversarial network |
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Computer and Information Science Indoor scene generation Radiance fields Super-resolution Generative adversarial network Yang, Yida Indoor scene generation method using radiance fields and super-resolution |
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Indoor scene generation in the digital realm has garnered significant attention within the computer vision domain, offering diverse applications ranging from architectural visualization to virtual reality experiences and gaming environments. Traditional methods relying on manual 3D modeling are time-consuming and lack scalability. Recent advancements, particularly the introduction of Neural Radiance Field (NeRF), have shown promise in representing indoor scenes comprehensively and synthesizing novel views. This Final Year Project (FYP) proposes a method combining NeRF-based scene generation with single image super-resolution methods. By leveraging a generative adversarial network (GAN) with radiance field and employing convolutional neural networks (CNNs) for super-resolution, the method aims to enhance the realism and resolution of generated indoor scenes. Experimental results demonstrate improvements over baseline models, although issues regarding consistency are noted and discussed. |
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Liu Ziwei |
author_facet |
Liu Ziwei Yang, Yida |
format |
Final Year Project |
author |
Yang, Yida |
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Yang, Yida |
title |
Indoor scene generation method using radiance fields and super-resolution |
title_short |
Indoor scene generation method using radiance fields and super-resolution |
title_full |
Indoor scene generation method using radiance fields and super-resolution |
title_fullStr |
Indoor scene generation method using radiance fields and super-resolution |
title_full_unstemmed |
Indoor scene generation method using radiance fields and super-resolution |
title_sort |
indoor scene generation method using radiance fields and super-resolution |
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Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/175309 |
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1806059839156125696 |