S2DNet : Learning Image Features for Accurate Sparse-to-Dense Matching

Hugo Germain¹, Guillaume Bourmaud², Vincent Lepetit¹

¹ LIGM - Ecole des Ponts, Univ Gustave Eiffel, CNRS, ESIEE Paris, France

² Laboratoire IMS, Université de Bordeaux, France

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.