Holographic displays are a promising technology for delivering immersive, true 3D visualization in virtual and augmented reality applications. However, generating high-fidelity phase-only holograms remains challenging, especially with the demand for efficient compression to handle the substantial data inherent in high-resolution holographic streaming. Existing techniques often struggle to balance the trade-off between optical display quality and compression efficiency, and jointly optimizing these aspects is still in its infancy. This work presents a learning-empowered multi-color hologram compression scheme that utilizes a pre-trained, camera-calibrated wave propagation model, especially for unfiltered holographic display configurations with compact form factors. In particular, the inter-color processing leverages the inherent redundancy across color channels, allowing for efficient compression. By incorporating the learned camera-calibrated wave propagation model into our training process, we can achieve superior optical display quality and compression rates. Experiments demonstrate that our method realizes a reduction in bits per pixel (bpp) of 44% to 74% over representative baselines at the same quality level. We envision the proposed compressive hologram synthesis scheme establishing a new benchmark for high-fidelity holographic reconstruction at lower bitrates, marking a significant advance towards the deployment of holography-empowered visual media systems.

Holography offers unique advantages for delivering perceptual realism while preserving compact form factors in VR/AR. Its perceptual quality, however, hinges on encoding rich wavefronts of photorealistic scenes into interference patterns and then incoherently multiplexing the resulting wave fields for perception. Existing CGH paradigms decouple radiance estimation from wave propagation by pre-rendering radiance on discretized scene sectors. This separation between radiometric and wave-optical computation inherently limits the range of focus cues and visual effects that can be faithfully reproduced, including depth- and view-continuity, and physically based material behaviors such as glossy or mirror-like reflection and refraction. We present a physically accurate yet computationally efficient wave optics rendering framework leveraging path tracing to encode full 3D visual cues into phase holograms. Specifically, we employ a Monte Carlo method to solve both the rendering equation and the Rayleigh-Sommerfeld integral simultaneously. Our algorithm is fully compatible with modern graphics techniques and can generate multiple time-multiplexed random holograms with minimal additional time cost via Path Reuse. By employing a fast approximation with an ambient radiance cache, we realize an order of magnitude convergence speed improvement. The resulting coherent wave fields that inherently encode comprehensive visual effects are converted into phase-only holograms under complex-amplitude supervision. Through extensive simulations and experimental validations on a spatial light modulator-based display prototype, we demonstrate faithful holographic reconstructions of natural 3D cues and complex materials, including realistic defocus blur, view-dependent effects, as well as appearance highlights and reflections.

Holographic displays offer the promise of realistic 3D visualization for virtual and augmented wearable solutions. Nevertheless, existing computer-generated holography (CGH) methods often struggle with either a high computational burden or limited display realism. While the emerging cloud-edge computing mechanism can enable the real-time streaming of holograms, classic image compression techniques struggle to efficiently encode and decode the substantial high-frequency information inherent in hologram data. In light of these challenges, we present a display-aware and lightweight CGH framework, leveraging implicit neural representations (INRs) and camera-calibrated wave propagation, to generate and compress high-fidelity phase-only holograms. Specifically, our approach interprets hologram generation as a continuous function approximation problem, enabling the network, with reduced parameters, to effectively learn the inherent periodicity and high-frequency components of 2D and 3D hologram data. To enable efficient deployment, we further incorporate quantization-aware training, followed by entropy coding. Experimental results evaluated on an unfiltered holographic display prototype demonstrate that the proposed INR-CGH retains image quality comparable to that of existing optimization-based methods in both 2D and 3D scenarios. In addition, our compact INR representation achieves up to 11× compression rate with minimal quality degradation and can be further reduced via quantization-aware training. The resulting model enables ≥250 fps in decoding speed, paving the way towards edge holography.

Holography has immense potential for near-eye displays in virtual and augmented reality (VR/AR), providing natural 3D depth cues through wavefront reconstruction. However, balancing the field of view (FOV) with the eyebox remains challenging, constrained by the étendue limitation. Additionally, holographic image quality is often compromised due to differences between actual wave propagation and simulation models. This study addresses these by expanding the eyebox via multi-angle illumination, and enhancing image quality with end-to-end pupil-aware hologram optimization. Further, energy efficiency is improved by incorporating higher-order diffractions and pupil constraints. We explore a Pupil-HOGD algorithm for multi-angle illumination and validate it with a dual-angle holographic display prototype. Integrated with camera calibration and tracked eye position, the developed Pupil-HOGD algorithm improves image quality and expands the eyebox by 50% horizontally. We envision this approach extends the space-bandwidth product (SBP) of holographic displays, enabling broader applications in immersive, high-quality visual computing.