Multi-illumination-interfered Neural Holography with Expanded Eyebox.

Area of Science:

• Optics and Photonics
• Computer Vision and Graphics
• Display Technology

Background:

• Holography offers natural 3D depth cues for virtual and augmented reality (VR/AR) near-eye displays via wavefront reconstruction.
• Key challenges include balancing field of view (FOV) and eyebox size due to étendue limitations, and maintaining holographic image quality.
• Existing simulation models often differ from actual wave propagation, impacting visual fidelity.

Purpose of the Study:

• To expand the eyebox and enhance holographic image quality in VR/AR displays.
• To improve energy efficiency by integrating higher-order diffractions and pupil constraints.
• To develop and validate a pupil-aware hologram optimization algorithm for improved visual computing.

Main Methods:

• Implemented multi-angle illumination to expand the eyebox.
• Employed end-to-end pupil-aware hologram optimization for enhanced image quality.
• Developed and validated the Pupil-HOGD algorithm using a dual-angle holographic display prototype with camera calibration and tracked eye position.

Main Results:

• The Pupil-HOGD algorithm successfully improved holographic image quality.
• Achieved a 50% horizontal expansion of the eyebox.
• Demonstrated improved energy efficiency through higher-order diffractions and pupil constraints.

Conclusions:

• The developed Pupil-HOGD algorithm effectively addresses limitations in holographic near-eye displays.
• This approach significantly enhances both image quality and eyebox size, crucial for immersive VR/AR experiences.
• The method has the potential to extend the space-bandwidth product (SBP) of holographic displays for broader applications.