@techreport{0361e89fc4e24c28a8477359ebaabfb4,
title = "S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching",
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.",
author = "Hugo Germain and Guillaume Bourmaud and Vincent Lepetit",
year = "2020",
month = apr,
day = "3",
doi = "https://arxiv.org/abs/2004.01673v1",
language = "English",
series = "arXiv.org e-Print archive",
publisher = "Cornell University Library",
type = "WorkingPaper",
institution = "Cornell University Library",
}