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Human visual system relies on both binocular stereo cues and monocularfocusness cues to gain effective 3D perception. In computer vision, the twoproblems are traditionally solved in separate tracks. In this paper, we presenta unified learning-based technique that simultaneously uses both types of cuesfor depth inference. Specifically, we use a pair of focal stacks as input toemulate human perception. We first construct a comprehensive focal stacktraining dataset synthesized by depth-guided light field rendering. We thenconstruct three individual networks: a Focus-Net to extract depth from a singlefocal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image fromthe focal stack, and a Stereo-Net to conduct stereo matching. We show how tointegrate them into a unified BDfF-Net to obtain high-quality depth maps.Comprehensive experiments show that our approach outperforms thestate-of-the-art in both accuracy and speed and effectively emulates humanvision systems.

Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs