  Depth estimation from light field (LF) images is a fundamental step for
numerous applications. Recently, learning-based methods have achieved higher
accuracy and efficiency than the traditional methods. However, it is costly to
obtain sufficient depth labels for supervised training. In this paper, we
propose an unsupervised framework to estimate depth from LF images. First, we
design a disparity estimation network (DispNet) with a coarse-to-fine structure
to predict disparity maps from different view combinations. It explicitly
performs multi-view feature matching to learn the correspondences effectively.
As occlusions may cause the violation of photo-consistency, we introduce an
occlusion prediction network (OccNet) to predict the occlusion maps, which are
used as the element-wise weights of photometric loss to solve the occlusion
issue and assist the disparity learning. With the disparity maps estimated by
multiple input combinations, we then propose a disparity fusion strategy based
on the estimated errors with effective occlusion handling to obtain the final
disparity map with higher accuracy. Experimental results demonstrate that our
method achieves superior performance on both the dense and sparse LF images,
and also shows better robustness and generalization on the real-world LF images
compared to the other methods.
