Computer-generated holography (CGH) provides volumetric control of coherent wavefront and is fundamental to applications such as volumetric 3D displays, lithography, neural photostimulation, and optical/acoustic trapping. Recently, deep learning-based methods emerged as promising computational paradigms for CGH synthesis that overcome the quality-runtime tradeoff in conventional simulation/optimization-based methods. Yet, the quality of the predicted hologram is intrinsically bounded by the dataset’s quality. Here we introduce a new hologram dataset, MIT-CGH-4K-V2, that uses a layered depth image as a data-efficient volumetric 3D input and a two-stage supervised+unsupervised training protocol for direct synthesis of high-quality 3D phase-only holograms. The proposed system also corrects vision aberration, allowing customization for end-users. We experimentally show photorealistic 3D holographic projections and discuss relevant spatial light modulator calibration procedures. Our method runs in real-time on a consumer GPU and 5 FPS on an iPhone 13 Pro, promising drastically enhanced performance for the applications above.

计算机生成的全息 (CGH) 提供相干波前的体积控制，是体积 3D 显示器、光刻、神经光刺激和光学/声学捕获等应用的基础。最近，基于深度学习的方法作为 CGH 合成的有前途的计算范式出现，克服了传统基于模拟/优化的方法中的质量-运行时权衡。然而，预测全息图的质量本质上受数据集质量的限制。在这里，我们介绍了一个新的全息数据集 MIT-CGH-4K-V2，它使用分层深度图像作为数据高效的体积 3D 输入和用于直接合成高质量 3D 相位的两阶段监督+无监督训练协议-只有全息图。所提出的系统还可以校正视觉像差，从而允许为最终用户定制。我们通过实验展示了逼真的 3D 全息投影并讨论了相关的空间光调制器校准程序。我们的方法在消费级 GPU 上实时运行，在 iPhone 13 Pro 上以 5 FPS 运行，有望显着提高上述应用程序的性能。