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

TL;DR

This paper introduces a new method to generate large, customizable light field datasets using Unity and C#, addressing the lack of such datasets for machine learning tasks and enabling advanced research in the field.

Contribution

The authors develop a novel, scalable approach for creating reproducible light field datasets with customizable hardware setups, facilitating deep learning research.

Findings

Generated datasets are scalable and reproducible.

Method accelerates light field deep learning research.

Customizable hardware configurations enhance dataset diversity.

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.