Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions
Author(s)
Zheng, Chuanjun; Zhan, Yicheng; Shi, Liang; Cakmakci, Ozan; Ak?it, Kaan
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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.
Description
SA Technical Communications ’24, December 03–06, 2024, Tokyo, Japan
Date issued
2024-12-03Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryPublisher
ACM|SIGGRAPH Asia 2024 Technical Communications
Citation
Zheng, Chuanjun, Zhan, Yicheng, Shi, Liang, Cakmakci, Ozan and Ak?it, Kaan. 2024. "Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions."
Version: Final published version
ISBN
979-8-4007-1140-4
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