  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.
