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Light field (LF) depth estimation plays a crucial role in many LF-basedapplications. Existing LF depth estimation methods consider depth estimation asa regression problem, where a pixel-wise L1 loss is employed to supervise thetraining process. However, the disparity map is only a sub-space projection(i.e., an expectation) of the disparity distribution, which is essential formodels to learn. In this paper, we propose a simple yet effective method tolearn the sub-pixel disparity distribution by fully utilizing the power of deepnetworks, especially for LF of narrow baselines. We construct the cost volumeat the sub-pixel level to produce a finer disparity distribution and design anuncertainty-aware focal loss to supervise the predicted disparity distributiontoward the ground truth. Extensive experimental results demonstrate theeffectiveness of our method.Our method significantly outperforms recentstate-of-the-art LF depth algorithms on the HCI 4D LF Benchmark in terms of allfour 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}.

Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation