TITLE: A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning 

ABSTRACT: 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.
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.
A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning
http://arxiv.org/abs/2212.06701v1