Papers › DKM: Dense Kernelized Feature Matching for Geometry Estimation
DKM: Dense Kernelized Feature Matching for Geometry Estimation
Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck, Michael Felsberg
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
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Matching | ZEB | DKM | Mean AUC@5° | 46.2 | #4 of 10 | Archive leaderboard | report |
| Pose Estimation | InLoc | DKM | DUC1-Acc@0.25m,10° | 51.5 | #4 of 6 | Archive leaderboard | report |
| Pose Estimation | InLoc | DKM | DUC1-Acc@0.5m,10° | 75.3 | #4 of 6 | Archive leaderboard | report |
| Pose Estimation | InLoc | DKM | DUC1-Acc@1.0m,10° | 86.9 | #4 of 6 | Archive leaderboard | report |
| Pose Estimation | InLoc | DKM | DUC2-Acc@0.25m,10° | 63.4 | #4 of 6 | Archive leaderboard | report |
| Pose Estimation | InLoc | DKM | DUC2-Acc@0.5m,10° | 82.4 | #4 of 6 | Archive leaderboard | report |
| Pose Estimation | InLoc | DKM | DUC2-Acc@1.0m,10° | 87.8 | #4 of 6 | Archive leaderboard | report |
| Visual Localization | Aachen Day-Night v1.1 Benchmark | DKM | Acc@0.25m, 2° | 70.2 | #7 of 7 | Archive leaderboard | report |
| Visual Localization | Aachen Day-Night v1.1 Benchmark | DKM | Acc@0.5m, 5° | 90.1 | #7 of 7 | Archive leaderboard | report |
| Visual Localization | Aachen Day-Night v1.1 Benchmark | DKM | Acc@5m, 10° | 97.4 | #7 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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