Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation

Existing light field (LF) depth estimation methods generally consider depth estimation as a regression problem, supervised by a pixel-wise L1 loss between the regressed disparity map and the groundtruth one. However, the disparity map is only a sub-space projection (i.e., an expectation) of the disparity distribution, while the latter one is more 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. In our method, we construct the cost volume at sub-pixel level to produce a finer depth distribution and design an uncertainty-aware focal loss to supervise the disparity distribution to be close to the groundtruth one. Extensive experimental results demonstrate the effectiveness of our method. Our method, called SubFocal, ranks the first place among 99 submitted algorithms on the HCI 4D LF Benchmark in terms of all the five accuracy metrics (i.e., BadPix0.01, BadPix0.03, BadPix0.07, MSE and Q25), and significantly outperforms recent state-of-the-art LF depth methods such as OACC-Net and AttMLFNet. Code and model are available at https://github.com/chaowentao/SubFocal.