  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.
