{"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/epinet-a-fully-convolutional-neural-network","title":"EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images","arxiv_id":"1804.02379","date":"2018-04-06","proceeding":"CVPR 2018 6","authors":["Changha Shin","Hae-Gon Jeon","Youngjin Yoon","In So Kweon","Seon Joo Kim"],"abstract":"Light field cameras capture both the spatial and the angular properties of\nlight rays in space. Due to its property, one can compute the depth from light\nfields in uncontrolled lighting environments, which is a big advantage over\nactive sensing devices. Depth computed from light fields can be used for many\napplications including 3D modelling and refocusing. However, light field images\nfrom hand-held cameras have very narrow baselines with noise, making the depth\nestimation difficult. any approaches have been proposed to overcome these\nlimitations for the light field depth estimation, but there is a clear\ntrade-off between the accuracy and the speed in these methods. In this paper,\nwe introduce a fast and accurate light field depth estimation method based on a\nfully-convolutional neural network. Our network is designed by considering the\nlight field geometry and we also overcome the lack of training data by\nproposing light field specific data augmentation methods. We achieved the top\nrank in the HCI 4D Light Field Benchmark on most metrics, and we also\ndemonstrate the effectiveness of the proposed method on real-world light-field\nimages.","url_abs":"http://arxiv.org/abs/1804.02379v1","url_pdf":"http://arxiv.org/pdf/1804.02379v1.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":"epinet-a-fully-convolutional-neural-network","repo_url":"https://github.com/chshin10/epinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"epinet-a-fully-convolutional-neural-network","repo_url":"https://github.com/rgmueller/EPINET-tf2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.02379","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}