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Light field cameras capture both the spatial and the angular properties oflight rays in space. Due to its property, one can compute the depth from lightfields in uncontrolled lighting environments, which is a big advantage overactive sensing devices. Depth computed from light fields can be used for manyapplications including 3D modelling and refocusing. However, light field imagesfrom hand-held cameras have very narrow baselines with noise, making the depthestimation difficult. any approaches have been proposed to overcome theselimitations for the light field depth estimation, but there is a cleartrade-off between the accuracy and the speed in these methods. In this paper,we introduce a fast and accurate light field depth estimation method based on afully-convolutional neural network. Our network is designed by considering thelight field geometry and we also overcome the lack of training data byproposing light field specific data augmentation methods. We achieved the toprank in the HCI 4D Light Field Benchmark on most metrics, and we alsodemonstrate the effectiveness of the proposed method on real-world light-fieldimages.

EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images