{"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/lf-net-learning-local-features-from-images","title":"LF-Net: Learning Local Features from Images","arxiv_id":"1805.09662","date":"2018-05-24","proceeding":"NeurIPS 2018 12","authors":["Yuki Ono","Eduard Trulls","Pascal Fua","Kwang Moo Yi"],"abstract":"We present a novel deep architecture and a training strategy to learn a local\nfeature pipeline from scratch, using collections of images without the need for\nhuman supervision. To do so we exploit depth and relative camera pose cues to\ncreate a virtual target that the network should achieve on one image, provided\nthe outputs of the network for the other image. While this process is\ninherently non-differentiable, we show that we can optimize the network in a\ntwo-branch setup by confining it to one branch, while preserving\ndifferentiability in the other. We train our method on both indoor and outdoor\ndatasets, with depth data from 3D sensors for the former, and depth estimates\nfrom an off-the-shelf Structure-from-Motion solution for the latter. Our models\noutperform the state of the art on sparse feature matching on both datasets,\nwhile running at 60+ fps for QVGA images.","url_abs":"http://arxiv.org/abs/1805.09662v2","url_pdf":"http://arxiv.org/pdf/1805.09662v2.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":"lf-net-learning-local-features-from-images","repo_url":"https://github.com/vcg-uvic/lf-net-release","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lf-net-learning-local-features-from-images","repo_url":"https://github.com/Messi1980/test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"lf-net-learning-local-features-from-images","repo_url":"https://github.com/alekseychuiko/lf-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"lf-net-learning-local-features-from-images","repo_url":"https://github.com/zoeyuchao/LFNet_modify","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.09662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09662"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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