{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bridging-unsupervised-and-supervised-depth","title":"Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision","arxiv_id":"2108.10843","date":"2021-08-24","proceeding":"ICCV 2021 10","authors":["Ning-Hsu Wang","Ren Wang","Yu-Lun Liu","Yu-Hao Huang","Yu-Lin Chang","Chia-Ping Chen","Kevin Jou"],"abstract":"Depth estimation is a long-lasting yet important task in computer vision. Most of the previous works try to estimate depth from input images and assume images are all-in-focus (AiF), which is less common in real-world applications. On the other hand, a few works take defocus blur into account and consider it as another cue for depth estimation. In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack). We design a shared architecture to exploit the relationship between depth and AiF estimation. As a result, the proposed method can be trained either supervisedly with ground truth depth, or \\emph{unsupervisedly} with AiF images as supervisory signals. 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