A Multispectral Light Field Dataset for Light Field Deep Learning

Citation Author(s):
Maximilian
Schambach
Karlsruhe Institute of Technology
Michael
Heizmann
Karlsruhe Institute of Technology
Submitted by:
Maximilian Schambach
Last updated:
Tue, 11/03/2020 - 05:05
DOI:
10.21227/y90t-xk47
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Abstract 

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.