  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
