Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions
Name
3681758.3697989.pdf
Size
8.2 MB
Format
Adobe PDF
Checksum (MD5)
6e2f809f0bf862c0c81ce75a1883109a
Author(s)
Zheng, Chuanjun
Zhan, Yicheng
Shi, Liang
Cakmakci, Ozan
Ak?it, Kaan
Date Issued
December 3, 2024
Publisher
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
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.
Description
SA Technical Communications ’24, December 03–06, 2024, Tokyo, Japan
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3681758.3697989
