Computer-Generated Holography (CGH) is a set of algorithmic methods for identifying holograms that reconstruct Three-Dimensional (3D) scenes in holographic displays. CGH algorithms decompose 3D scenes into multiplanes at different depth levels and rely on simulations of light that propagated from a source plane to a targeted plane. Thus, for n planes, CGH typically optimizes holograms using n plane-to-plane light transport simulations, leading to major time and computational demands. Our work replaces multiple planes with a focal surface and introduces a learned light transport model that could propagate a light field from a source plane to the focal surface in a single inference. Our learned light transport model leverages spatially adaptive convolution to achieve depth-varying propagation demanded by targeted focal surfaces. The proposed model reduces the hologram optimization process up to 1.5x, which contributes to hologram dataset generation and the training of future learned CGH models.

翻译：计算机生成全息术（CGH）是一套用于确定在全息显示器中重建三维（3D）场景的全息图的算法方法。CGH算法将3D场景分解为不同深度层的多平面，并依赖于模拟光从源平面传播到目标平面的过程。因此，对于n个平面，CGH通常需要使用n次平面到平面的光传输模拟来优化全息图，这导致了巨大的时间和计算需求。我们的工作用焦面取代了多个平面，并引入了一种学习型光传输模型，该模型可以在单次推理中将光场从源平面传播到焦面。我们的学习型光传输模型利用空间自适应卷积来实现目标焦面所需的深度变化传播。所提出的模型将全息图优化过程减少了高达1.5倍，这有助于全息图数据集的生成以及未来学习型CGH模型的训练。