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Unsupervised Depth Estimation from Light Field Using a Convolutional Neural Network

This paper proposes an unsupervised CNN-based method for explicit depth estimation from light field, which learns an end-to-end mapping from a 4D light field to the corresponding disparity map without the supervision of groundtruth depth. Specifically, we design a combined loss function imposing both compliance and divergence constraints on the warped sub-aperture images to the central view, which guarantees our network to generate an accurate and robust disparity map. Furthermore, we find that increasing the number of referenced views in depth feature extraction and complementing missing information caused by warping greatly boost the performance of our network. Due to the difficulty of obtaining groundtruth depth of real-world scenes in practice, the proposed method is much more feasible than supervised learning. On the other hand, compared with traditional non-learning methods, the proposed method better exploits the correlations in the 4D light field and generates superior depth results both quantitatively and qualitatively. Also, the proposed method helps improve the performance of subsequent applications based on the estimated depth, e.g., spatial super-resolution of light field.