  Matching cost construction is a key step in light field (LF) depth
estimation, but was rarely studied in the deep learning era. Recent deep
learning-based LF depth estimation methods construct matching cost by
sequentially shifting each sub-aperture image (SAI) with a series of predefined
offsets, which is complex and time-consuming. In this paper, we propose a
simple and fast cost constructor to construct matching cost for LF depth
estimation. Our cost constructor is composed by a series of convolutions with
specifically designed dilation rates. By applying our cost constructor to SAI
arrays, pixels under predefined disparities can be integrated and matching cost
can be constructed without using any shifting operation. More importantly, the
proposed cost constructor is occlusion-aware and can handle occlusions by
dynamically modulating pixels from different views. Based on the proposed cost
constructor, we develop a deep network for LF depth estimation. Our network
ranks first on the commonly used 4D LF benchmark in terms of the mean square
error (MSE), and achieves a faster running time than other state-of-the-art
methods.
