Zheng, C;
Zhan, Y;
Shi, L;
Cakmakci, O;
Akşit, K;
(2024)
Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions.
In:
Proceedings of the SA '24: SIGGRAPH Asia 2024 Technical Communications.
(pp. pp. 1-4).
ACM (Association for Computing Machinery)
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Abstract
Computer-Generated Holography (CGH) is a set of algorithmic methods for identifying holograms that reconstruct Three-Dimensio-nal (3D) scenes in holographic displays. CGH algorithms decompose 3D scenes into multiplanes at different depth levels and rely on simulations of light that propagated from a source plane to a targeted plane. Thus, for n planes, CGH typically optimizes holograms using n plane-To-plane light transport simulations, leading to major time and computational demands. Our work replaces multiple planes with a focal surface and introduces a learned light transport model that could propagate a light field from a source plane to the focal surface in a single inference. Our model leverages spatially adaptive convolution to achieve depth-varying propagation demanded by targeted focal surfaces. The proposed model reduces the hologram optimization process up to 1.5x, which contributes to hologram dataset generation and the training of future learned CGH models.
Type: | Proceedings paper |
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Title: | Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions |
Event: | SA '24: SIGGRAPH Asia 2024 Technical Communications |
ISBN-13: | 9798400711404 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1145/3681758.3697989 |
Publisher version: | https://doi.org/10.1145/3681758.3697989 |
Language: | English |
Additional information: | © 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/). |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10203842 |
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