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.
@article{arxiv.2212.06701,
title = {A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning},
author = {Julia Huang and Toure Smith and Aloukika Patro and Vidhi Chhabra},
journal= {arXiv preprint arXiv:2212.06701},
year = {2022}
}
Comments
5 pages, 5 figures, accepted to and presented at MIT URTC Conference, and will be published in IEEE proceedings