{"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/deep-depth-from-focus","title":"Deep Depth From Focus","arxiv_id":"1704.01085","date":"2017-04-04","proceeding":null,"authors":["Caner Hazirbas","Sebastian Georg Soyer","Maximilian Christian Staab","Laura Leal-Taixé","Daniel Cremers"],"abstract":"Depth from focus (DFF) is one of the classical ill-posed inverse problems in\ncomputer vision. Most approaches recover the depth at each pixel based on the\nfocal setting which exhibits maximal sharpness. Yet, it is not obvious how to\nreliably estimate the sharpness level, particularly in low-textured areas. In\nthis paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end\nlearning approach to this problem. One of the main challenges we face is the\nhunger for data of deep neural networks. In order to obtain a significant\namount of focal stacks with corresponding groundtruth depth, we propose to\nleverage a light-field camera with a co-calibrated RGB-D sensor. This allows us\nto digitally create focal stacks of varying sizes. Compared to existing\nbenchmarks our dataset is 25 times larger, enabling the use of machine learning\nfor this inverse problem. We compare our results with state-of-the-art DFF\nmethods and we also analyze the effect of several key deep architectural\ncomponents. These experiments show that our proposed method `DDFFNet' achieves\nstate-of-the-art performance in all scenes, reducing depth error by more than\n75% compared to the classical DFF methods.","url_abs":"http://arxiv.org/abs/1704.01085v3","url_pdf":"http://arxiv.org/pdf/1704.01085v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-depth-from-focus","repo_url":"https://github.com/MaximilianStaab/mDFF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deep-depth-from-focus","repo_url":"https://github.com/fuy34/dfv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-depth-from-focus","repo_url":"https://github.com/gameover27/ddff-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deep-depth-from-focus","repo_url":"https://github.com/gritYCDA/ddff-improve","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deep-depth-from-focus","repo_url":"https://github.com/soyers/ddff-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.01085","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}