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- Publisher Website: 10.1145/3388534.3407295
- Scopus: eid_2-s2.0-85090398564
- WOS: WOS:000684182700011
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Conference Paper: Neural Holography
Title | Neural Holography |
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Authors | |
Keywords | computational displays holography virtual and augmented reality |
Issue Date | 2020 |
Citation | ACM SIGGRAPH 2020 Emerging Technologies, SIGGRAPH 2020, 2020, article no. 3407295 How to Cite? |
Abstract | Holographic displays promise unprecedented capabilities for direct-view displays as well as virtual and augmented reality (VR/AR) applications. However, one of the biggest challenges for computer-generated holography (CGH) is the fundamental tradeoff between algorithm runtime and achieved image quality, which has prevented high-quality holographic image synthesis at fast speeds. Moreover, the image quality achieved by most holographic displays is low, due to the mismatch between physical light transport of the display and its simulated model. Here, we develop an algorithmic CGH framework that achieves unprecedented image fidelity and real-time framerates. Our framework comprises several parts, including a novel camera-in-the-loop optimization strategy that allows us to either optimize a hologram directly or train an interpretable model of the physical light transport and a neural network architecture that represents the first CGH algorithm capable of generating full-color holographic images at 1080p resolution in real time. |
Persistent Identifier | http://hdl.handle.net/10722/315331 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Peng, Yifan | - |
dc.contributor.author | Choi, Suyeon | - |
dc.contributor.author | Padmanaban, Nitish | - |
dc.contributor.author | Kim, Jonghyun | - |
dc.contributor.author | Wetzstein, Gordon | - |
dc.date.accessioned | 2022-08-05T10:18:30Z | - |
dc.date.available | 2022-08-05T10:18:30Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | ACM SIGGRAPH 2020 Emerging Technologies, SIGGRAPH 2020, 2020, article no. 3407295 | - |
dc.identifier.uri | http://hdl.handle.net/10722/315331 | - |
dc.description.abstract | Holographic displays promise unprecedented capabilities for direct-view displays as well as virtual and augmented reality (VR/AR) applications. However, one of the biggest challenges for computer-generated holography (CGH) is the fundamental tradeoff between algorithm runtime and achieved image quality, which has prevented high-quality holographic image synthesis at fast speeds. Moreover, the image quality achieved by most holographic displays is low, due to the mismatch between physical light transport of the display and its simulated model. Here, we develop an algorithmic CGH framework that achieves unprecedented image fidelity and real-time framerates. Our framework comprises several parts, including a novel camera-in-the-loop optimization strategy that allows us to either optimize a hologram directly or train an interpretable model of the physical light transport and a neural network architecture that represents the first CGH algorithm capable of generating full-color holographic images at 1080p resolution in real time. | - |
dc.language | eng | - |
dc.relation.ispartof | ACM SIGGRAPH 2020 Emerging Technologies, SIGGRAPH 2020 | - |
dc.subject | computational displays | - |
dc.subject | holography | - |
dc.subject | virtual and augmented reality | - |
dc.title | Neural Holography | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/3388534.3407295 | - |
dc.identifier.scopus | eid_2-s2.0-85090398564 | - |
dc.identifier.spage | article no. 3407295 | - |
dc.identifier.epage | article no. 3407295 | - |
dc.identifier.isi | WOS:000684182700011 | - |