论文标题
一种用于生成机器学习的可自定义光场数据集的新方法
A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning
论文作者
论文摘要
为了训练深度学习模型（通常超过传统方法），在许多领域都使用了指定介质的大数据集，例如图像。但是，对于特定于光场的机器学习任务，缺乏此类可用数据集。因此，我们创建了自己的光场数据集，与单数图像相比，由于光场中的大量信息，它们具有各种应用的潜力。使用Unity和C＃框架，我们开发了一种新颖的方法，用于基于可自定义的硬件配置生成大型，可扩展和可重现的光场数据集，以加速光场深度学习研究。
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.
