{"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-kernelized-dense-geometric-matching","title":"DKM: Dense Kernelized Feature Matching for Geometry Estimation","arxiv_id":"2202.00667","date":"2022-02-01","proceeding":"CVPR 2023 1","authors":["Johan Edstedt","Ioannis Athanasiadis","Mårten Wadenbäck","Michael Felsberg"],"abstract":"Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, \\textbf{D}ense \\textbf{K}ernelized Feature \\textbf{M}atching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC$@5^{\\circ}$ compared to the best previous sparse method and dense method respectively. Our code is provided at https://github.com/Parskatt/dkm","url_abs":"https://arxiv.org/abs/2202.00667v3","url_pdf":"https://arxiv.org/pdf/2202.00667v3.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-kernelized-dense-geometric-matching","repo_url":"https://github.com/parskatt/dkm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"camera-pose-estimation","task_name":"Camera Pose Estimation"},{"task_slug":"geometric-matching","task_name":"Geometric Matching"},{"task_slug":"image-matching","task_name":"Image Matching"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"visual-localization","task_name":"Visual Localization"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-matching-on-zeb","task":"Image Matching","dataset":"ZEB","model":"DKM","rank_in_archive_order":4,"of":10,"metrics":{"Mean AUC@5°":"46.2"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-inloc","task":"Pose Estimation","dataset":"InLoc","model":"DKM","rank_in_archive_order":4,"of":6,"metrics":{"DUC1-Acc@0.25m,10°":"51.5","DUC1-Acc@0.5m,10°":"75.3","DUC1-Acc@1.0m,10°":"86.9","DUC2-Acc@0.25m,10°":"63.4","DUC2-Acc@0.5m,10°":"82.4","DUC2-Acc@1.0m,10°":"87.8"},"uses_additional_data":false},{"leaderboard":"/sota/visual-localization-on-aachen-day-night-v1-1","task":"Visual Localization","dataset":"Aachen Day-Night v1.1 Benchmark","model":"DKM","rank_in_archive_order":7,"of":7,"metrics":{"Acc@0.25m, 2°":"70.2","Acc@0.5m, 5°":"90.1","Acc@5m, 10°":"97.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.00667","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}