S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching

Hugo Germain; Guillaume Bourmaud; Vincent Lepetit

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arXiv · 2004.01673

S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching

Hugo Germain·Guillaume Bourmaud·Vincent Lepetit

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Abstract

Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust correspondences under challenging conditions, they are often limited in terms of precision. In this paper, we introduce S2DNet, a novel feature matching pipeline, designed and trained to efficiently establish both robust and accurate correspondences. By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-term visual localization datasets.

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Hugo Germain, Guillaume Bourmaud, Vincent Lepetit. 2020-04-03. S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching. https://arxiv.org/abs/2004.01673

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