EPINET: A Fully-Convolutional Neural NetworkUsing Epipolar Geometry for Depth from Light Field Images
Abstract
Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lighting environments, which is a big advantage over
active sensing devices. Depth computed from light fields
can be used for many applications including 3D modelling
and refocusing. However, light field images from hand-held
cameras have very narrow baselines with noise, making the
depth estimation difficult. Many approaches have been proposed to overcome these limitations for the light field depth
estimation, but there is a clear trade-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 a fully-convolutional neural network. Our
network is designed by considering the light field geometry
and we also overcome the lack of training data by proposing
light field specific data augmentation methods. We achieved
the top rank in the HCI 4D Light Field Benchmark on most
metrics, and we also demonstrate the effectiveness of the
proposed method on real-world light-field images.