10.21227/DZ07-ZD28
Maximilian Schambach
Maximilian
Schambach
0000-0002-4927-266X
Karlsruhe Institute of Technology
Michael Heizmann
Michael
Heizmann
0000-0001-9339-2055
Karlsruhe Institute of Technology
A Multispectral Light Field Dataset for Light Field Deep Learning
IEEE DataPort
2020
Image Processing
Computer Vision
Machine Learning
Light field
2020-10-16
Open Access Dataset
Creative Commons Attribution
Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth. The dataset consists of a training, validation and test dataset, containing light fields of randomly generated scenes, as well as a challenge dataset rendered from hand-crafted scenes enabling detailed performance assessment.