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

Julia Huang; Toure Smith; Aloukika Patro; Vidhi Chhabra

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arXiv · 2212.06701

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

Julia Huang·Toure Smith·Aloukika Patro·Vidhi Chhabra

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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.

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BibTeX RIS

Julia Huang, Toure Smith, Aloukika Patro, Vidhi Chhabra. 2022-12-13. A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning. https://arxiv.org/abs/2212.06701

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