  Light field (LF) depth estimation plays a crucial role in many LF-based
applications. Existing LF depth estimation methods consider depth estimation as
a regression problem, where a pixel-wise L1 loss is employed to supervise the
training process. However, the disparity map is only a sub-space projection
(i.e., an expectation) of the disparity distribution, which is essential for
models to learn. In this paper, we propose a simple yet effective method to
learn the sub-pixel disparity distribution by fully utilizing the power of deep
networks, especially for LF of narrow baselines. We construct the cost volume
at the sub-pixel level to produce a finer disparity distribution and design an
uncertainty-aware focal loss to supervise the predicted disparity distribution
toward the ground truth. Extensive experimental results demonstrate the
effectiveness of our method.Our method significantly outperforms recent
state-of-the-art LF depth algorithms on the HCI 4D LF Benchmark in terms of all
four accuracy metrics (i.e., BadPix 0.01, BadPix 0.03, BadPix 0.07, and MSE
$\times$100). The code and model of the proposed method are available at
\url{https://github.com/chaowentao/SubFocal}.
