To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

翻译：深层次学习模式往往优于传统方法,为了培训深层次学习模式,许多领域都使用特定介质(如图像)的大型数据集,然而,对于针对特定实地的轻度机器学习任务,则缺乏此类可用数据集。因此,我们创建了自己的光度实地数据集,由于光度地区的信息比单图像丰富,这些数据集在各种应用方面具有巨大潜力。我们利用“团结”和“C#”框架,开发了一种新颖的方法,在可定制硬件配置的基础上生成大型、可缩放和可复制的光度实地数据集,以加速光深层学习研究。