Neural Holography with Camera-in-the-loop Training
Event Type
Technical Papers
Technical Papers Q&A
Registration Categories
TimeThursday, 10 December 202013:07 - 13:15 SGT
LocationZoom Room 4
DescriptionHolographic displays promise unprecedented capabilities for direct-view
displays as well as virtual and augmented reality applications. However, one
of the biggest challenges for computer-generated holography (CGH) is the
fundamental tradeoff between algorithm runtime and achieved image quality,
which has prevented high-quality holographic image synthesis at fast speeds.
Moreover, the image quality achieved by most holographic displays is low,
due to the mismatch between the optical wave propagation of the display
and its simulated model. Here, we develop an algorithmic CGH framework
that achieves unprecedented image fidelity and real-time framerates. Our
framework comprises several parts, including a novel camera-in-the-loop
optimization strategy that allows us to either optimize a hologram directly
or train an interpretable model of the optical wave propagation and a neural
network architecture that represents the first CGH algorithm capable of
generating full-color high-quality holographic images at 1080p resolution in
real time.