@inproceedings{7fe0435dcd7d4b7c89874b1acf1f41a8,
title = "S2DNet: Learning Image Features for Accurate Sparse-to-Dense 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.",
keywords = "Classification, Feature matching, Visual localization",
author = "Hugo Germain and Guillaume Bourmaud and Vincent Lepetit",
year = "2020",
month = aug,
day = "23",
doi = "10.1007/978-3-030-58580-8_37",
language = "English",
isbn = "9783030585792",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "626--643",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings",
note = "16th European Conference on Computer Vision : ECCV 2020, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
}