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• W4386075582 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, Dense Kernelized Feature Matching, 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° compared to the best previous sparse method and dense method respectively. Our code is provided at the following repository: https://github.com/Parskatt/DKM." @default.
• W4386075582 created "2023-08-23" @default.
• W4386075582 creator A5042087981 @default.
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• W4386075582 date "2023-06-01" @default.
• W4386075582 modified "2023-09-27" @default.
• W4386075582 title "DKM: Dense Kernelized Feature Matching for Geometry Estimation" @default.
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• W4386075582 doi "https://doi.org/10.1109/cvpr52729.2023.01704" @default.
• W4386075582 hasPublicationYear "2023" @default.
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