Holographic near-eye displays offer unprecedented capabilities for virtual and augmented reality systems, including perceptually important focus cues. Although artificial intelligence--driven algorithms for computer-generated holography (CGH) have recently made much progress in improving the image quality and synthesis efficiency of holograms, these algorithms are not directly applicable to emerging phase-only spatial light modulators (SLM) that are extremely fast but offer phase control with very limited precision. The speed of these SLMs offers time multiplexing capabilities, essentially enabling partially-coherent holographic display modes. Here we report advances in camera-calibrated wave propagation models for these types of holographic near-eye displays and we develop a CGH framework that robustly optimizes the heavily quantized phase patterns of fast SLMs. Our framework is flexible in supporting runtime supervision with different types of content, including 2D and 2.5D RGBD images, 3D focal stacks, and 4D light fields. Using our framework, we demonstrate state-of-the-art results for all of these scenarios in simulation and experiment.

翻译：虽然计算机生成的全息摄影(CGH)人工智能驱动算法最近在提高全息图像质量和全息图像合成效率方面取得了很大进展,但这些算法并不直接适用于正在形成的、且速度极快但能以非常有限的精确度提供阶段控制的、仅具有虚拟和增强现实系统的全眼显示器。这些可持续土地管理的速度提供了时间倍增能力,基本上能够实现部分一致的全息显示模式。在这里,我们报告了这些类型的全息全息显示的摄影校正波传播模型的进展,我们开发了一个能够强有力地优化快速解运高度四分化阶段模式的CGH框架。我们的框架灵活地支持对不同类型内容的运行时间监督,包括2D和2.5D RGBD图像、3D焦点堆和4D光场。我们利用我们的框架,在模拟和实验中展示所有这些情景的最新效果。