Emerging learned holography approaches have enabled faster and high-quality hologram synthesis, setting a new milestone toward practical holographic displays. However, these learned models require training a dedicated model for each set of display-scene parameters. To address this shortcoming, our work introduces a highly configurable learned model structure, synthesizing 3D holograms interactively while supporting diverse display-scene parameters. Our family of models relying on this structure can be conditioned continuously for varying novel scene parameters, including input images, propagation distances, volume depths, peak brightnesses, and novel display parameters of pixel pitches and wavelengths. Uniquely, our findings unearth a correlation between depth estimation and hologram synthesis tasks in the learning domain, leading to a learned model that unlocks accurate 3D hologram generation from 2D images across varied display-scene parameters. We validate our models by synthesizing high-quality 3D holograms in simulations and also verify our findings with two different holographic display prototypes. Moreover, our family of models can synthesize holograms with a 2x speed-up compared to the state-of-the-art learned holography approaches in the literature.

翻译：新兴的基于学习的全息术方法已实现更快速、高质量的全息图合成，为实用化全息显示设立了新的里程碑。然而，这些学习模型需要针对每组显示-场景参数训练专用模型。为克服这一局限，本研究提出了一种高度可配置的学习模型结构，能够交互式合成三维全息图，同时支持多样化的显示-场景参数。基于该结构的模型系列可连续调节以适应变化的新场景参数，包括输入图像、传播距离、体深度、峰值亮度，以及像素间距和波长等新型显示参数。特别地，我们的研究揭示了学习领域中深度估计与全息图合成任务之间的关联性，由此开发的学习模型能够从二维图像生成精确的三维全息图，并适应不同的显示-场景参数。我们通过仿真合成高质量三维全息图验证了模型性能，并利用两种不同的全息显示原型机验证了研究发现。此外，与文献中最先进的基于学习的全息术方法相比，我们的模型系列能以两倍的速度合成全息图。