Neural Étendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display

Code

• github.com/nseungwoo/depolarized-holography
  pytorch★ 14

Abstract

Holographic displays can generate light fields by dynamically modulating the wavefront of a coherent beam of light using a spatial light modulator, promising rich virtual and augmented reality applications. However, the limited spatial resolution of existing dynamic spatial light modulators imposes a tight bound on the diffraction angle. As a result, modern holographic displays possess low \'etendue, which is the product of the display area and the maximum solid angle of diffracted light. The low \'etendue forces a sacrifice of either the field-of-view (FOV) or the display size. In this work, we lift this limitation by presenting neural \'etendue expanders. This new breed of optical elements, which is learned from a natural image dataset, enables higher diffraction angles for ultra-wide FOV while maintaining both a compact form factor and the fidelity of displayed contents to human viewers. With neural \'etendue expanders, we experimentally achieve 64 \'etendue expansion of natural images in full color, expanding the FOV by an order of magnitude horizontally and vertically, with high-fidelity reconstruction quality (measured in PSNR) over 29 dB on retinal-resolution images.

Tasks

Reproductions