Stochastic Light Field Holography
Florian Schiffers Praneeth Chakravarthula Nathan Matsuda Grace Kuo Ethan Tseng Douglas Lanman Felix Heide Oliver Cossairt
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 \'etendue 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 significantly
improves the realism of the viewing experience for a variety of different pupil
states.