Surrounding-aware screen-space-global-illumination using generative adversarial network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| Ngôn ngữ: | English |
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Brac University
2023
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/21832 |
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10361-21832 |
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10361-218322023-10-16T21:04:06Z Surrounding-aware screen-space-global-illumination using generative adversarial network Mahmud, Abrar Sifar, Alimus Rahman, Moh. Absar Mostafa, Fateen Yusuf Tasnova, Lamia Mukta, Jannatun Noor Department of Computer Science and Engineering, BRAC University Computer graphics Global illumination Neural networks GAN Neuropsychology Rendering (Computer graphics) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 57-59). Global Illumination is a strategy in computer graphics to add certain degree of realism in case of 3D scene lighting, by trying to emulate how light rays work in real life. Several approaches exists to achieve such kind of visual effect for computed generated imagery. The most physically accurate approach is through ray-tracing. It can produce results which are realistic enough, with a trade-off of being time and computational-resource intensive, making them unsuitable for real-time usage. For more real-time usage scenarios, a set of faster algorithm exists that utilizes rasterization rather than ray-tracing. Despite being faster, those still can be resource intensive or generate physically inaccurate results. Our Generative Adversarial Net-work based approach targets to bring close to physically accurate results based on rasterization output data which can be obtained from a conventional deferred rendering pipeline, while retaining speed. These rasterization output data, which are basically screen-space feature buffers will act as the input to our deep-learning network, which in turn will produce per-frame lightmaps that contain global illumination data, which are further used to generate a presentable frame on the screen. Using screen-space information from a single viewpoint won’t always guarantee light consistency, thus our approach takes into account the rasterization output data of the surrounding of a certain viewpoint, producing more accurate global illumination. Abrar Mahmud Alimus Sifar Moh. Absar Rahman Fateen Yusuf Mostafa Lamia Tasnova B.Sc. in Computer Science and Engineering 2023-10-16T04:24:43Z 2023-10-16T04:24:43Z ©2022 2022-09-20 Thesis ID 18201147 ID 18201157 ID 18201167 ID 18201200 ID 18301053 http://hdl.handle.net/10361/21832 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 71 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Computer graphics Global illumination Neural networks GAN Neuropsychology Rendering (Computer graphics) |
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Computer graphics Global illumination Neural networks GAN Neuropsychology Rendering (Computer graphics) Mahmud, Abrar Sifar, Alimus Rahman, Moh. Absar Mostafa, Fateen Yusuf Tasnova, Lamia Surrounding-aware screen-space-global-illumination using generative adversarial network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Mukta, Jannatun Noor |
| author_facet |
Mukta, Jannatun Noor Mahmud, Abrar Sifar, Alimus Rahman, Moh. Absar Mostafa, Fateen Yusuf Tasnova, Lamia |
| format |
Thesis |
| author |
Mahmud, Abrar Sifar, Alimus Rahman, Moh. Absar Mostafa, Fateen Yusuf Tasnova, Lamia |
| author_sort |
Mahmud, Abrar |
| title |
Surrounding-aware screen-space-global-illumination using generative adversarial network |
| title_short |
Surrounding-aware screen-space-global-illumination using generative adversarial network |
| title_full |
Surrounding-aware screen-space-global-illumination using generative adversarial network |
| title_fullStr |
Surrounding-aware screen-space-global-illumination using generative adversarial network |
| title_full_unstemmed |
Surrounding-aware screen-space-global-illumination using generative adversarial network |
| title_sort |
surrounding-aware screen-space-global-illumination using generative adversarial network |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21832 |
| work_keys_str_mv |
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| _version_ |
1814307438919155712 |