  Exploiting light field data makes it possible to obtain dense and accurate
depth map. However, synthetic scenes with limited disparity range cannot
contain the diversity of real scenes. By training in synthetic data, current
learning-based methods do not perform well in real scenes. In this paper, we
propose a self-supervised learning framework for light field depth estimation.
Different from the existing end-to-end training methods using disparity label
per pixel, our approach implements network training by estimating EPI disparity
shift after refocusing, which extends the disparity range of epipolar lines. To
reduce the sensitivity of EPI to noise, we propose a new input mode called
EPI-Stack, which stacks EPIs in the view dimension. This method is less
sensitive to noise scenes than traditional input mode and improves the
efficiency of estimation. Compared with other state-of-the-art methods, the
proposed method can also obtain higher quality results in real-world scenarios,
especially in the complex occlusion and depth discontinuity.
