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Learning-based 3D computer-generated holography – Liang Shi

June 2, 2021 - 4:30pm
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Abstract: Computer generated holography (CGH) is fundamental to
applications such as biosensing, volumetric display, optical/acoustic
tweezer, security and many others that require spatial control of
intricate optical or acoustic fields. For near-eye displays, CGH
provides the opportunity to support true 3D projection in a
sunglass-like display. Yet, the conventional approach to compute a true
3D hologram via physical simulation of diffraction and inference is slow
and unaware of occlusion. Moreover, experimental results are often
inferior to simulations due to non-idealized optical systems, non-linear
and non-uniform SLM responses, and image degradation caused by complex
to phase-only conversion. These computational and hardware-imposed
challenges together limit the interactiveness and realism of the
ultimate immersive experience. In this talk, I will describe techniques
to mitigate these challenges, including physical simulation algorithms
that handle occlusion for RGB-D and more advanced 3D input, methods to
create large-scale 3D hologram datasets, training of CNNs to speed up
complex and phase-only hologram synthesis, and approaches to compensate
hardware limitations. Together, the resulted system can synthesis and
display photorealistic 3D holograms in real-time using a single
consumer-grade GPU and run interactively on an iPhone leveraging the
Neural Engine. I will further discuss possible extensions that could be
built top of the proposed system to support foveated rendering, static
pupil expansion, view-dependent effect and other features.

Bio: Liang Shi is a PhD student in Prof. Wojciech Matusik’s
Computational Fabrication and Design Group at MIT CSAIL. He received
his B.E. from Beihang University, M.Sc. from Stanford University, and
was a member of Prof. Gordon Wetzstein’s Computational Imaging Lab.  He
was a research intern at NVIDIA, Adobe, Facebook Reality Lab. His
current research interests include computational display, fabrication,
and appearance modeling.

Event Sponsor: 
SCIEN Stanford Center for Image System Engineering