{"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/liff-light-field-features-in-scale-and-depth","title":"LiFF: Light Field Features in Scale and Depth","arxiv_id":"1901.03916","date":"2019-01-13","proceeding":"CVPR 2019 6","authors":["Donald G. Dansereau","Bernd Girod","Gordon Wetzstein"],"abstract":"Feature detectors and descriptors are key low-level vision tools that many\nhigher-level tasks build on. Unfortunately these fail in the presence of\nchallenging light transport effects including partial occlusion, low contrast,\nand reflective or refractive surfaces. Building on spatio-angular imaging\nmodalities offered by emerging light field cameras, we introduce a new and\ncomputationally efficient 4D light field feature detector and descriptor: LiFF.\nLiFF is scale invariant and utilizes the full 4D light field to detect features\nthat are robust to changes in perspective. This is particularly useful for\nstructure from motion (SfM) and other tasks that match features across\nviewpoints of a scene. We demonstrate significantly improved 3D reconstructions\nvia SfM when using LiFF instead of the leading 2D or 4D features, and show that\nLiFF runs an order of magnitude faster than the leading 4D approach. Finally,\nLiFF inherently estimates depth for each feature, opening a path for future\nresearch in light field-based SfM.","url_abs":"http://arxiv.org/abs/1901.03916v1","url_pdf":"http://arxiv.org/pdf/1901.03916v1.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":"liff-light-field-features-in-scale-and-depth","repo_url":"https://github.com/doda42/LiFF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.03916","atlas_url":"https://app.syntology.ai/?focus=1901.03916","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}