TY - GEN
T1 - Stochastic Light Field Holography
AU - Schiffers, Florian
AU - Chakravarthula, Praneeth
AU - Matsuda, Nathan
AU - Kuo, Grace
AU - Tseng, Ethan
AU - Lanman, Douglas
AU - Heide, Felix
AU - Cossairt, Oliver
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The Visual Turing Test is the ultimate goal to evaluate the realism of holographic displays. Previous studies have focused on addressing challenges such as limited étendue and image quality over a large focal volume, but they have not investigated the effect of pupil sampling on the viewing experience in full 3D holograms. In this work, we tackle this problem with a novel hologram generation algorithm motivated by matching the projection operators of incoherent (Light Field) and coherent (Wigner Function) light transport. To this end, we supervise hologram computation using synthesized photographs, which are rendered on-The-fly using Light Field refocusing from stochastically sampled pupil states during optimization. The proposed method produces holograms with correct parallax and focus cues, which are important for passing the Visual Turing Test. We validate that our approach compares favorably to state-of-The-Art CGH algorithms that use Light Field and Focal Stack supervision. Our experiments demonstrate that our algorithm improves the viewing experience when evaluated under a large variety of different pupil states.
AB - The Visual Turing Test is the ultimate goal to evaluate the realism of holographic displays. Previous studies have focused on addressing challenges such as limited étendue and image quality over a large focal volume, but they have not investigated the effect of pupil sampling on the viewing experience in full 3D holograms. In this work, we tackle this problem with a novel hologram generation algorithm motivated by matching the projection operators of incoherent (Light Field) and coherent (Wigner Function) light transport. To this end, we supervise hologram computation using synthesized photographs, which are rendered on-The-fly using Light Field refocusing from stochastically sampled pupil states during optimization. The proposed method produces holograms with correct parallax and focus cues, which are important for passing the Visual Turing Test. We validate that our approach compares favorably to state-of-The-Art CGH algorithms that use Light Field and Focal Stack supervision. Our experiments demonstrate that our algorithm improves the viewing experience when evaluated under a large variety of different pupil states.
KW - Computational Display
KW - Holography
KW - Light Field
KW - Near-Eye Display
KW - VR/AR
KW - Wigner Distributions
UR - http://www.scopus.com/inward/record.url?scp=85172871907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172871907&partnerID=8YFLogxK
U2 - 10.1109/ICCP56744.2023.10233716
DO - 10.1109/ICCP56744.2023.10233716
M3 - Conference contribution
AN - SCOPUS:85172871907
T3 - IEEE International Conference on Computational Photography, ICCP 2023
BT - IEEE International Conference on Computational Photography, ICCP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computational Photography, ICCP 2023
Y2 - 28 July 2023 through 30 July 2023
ER -