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

Hugo Germain*, Guillaume Bourmaud, Vincent Lepetit

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer, Cham
Pages626-643
Number of pages18
ISBN (Print)9783030585792
DOIs
Publication statusPublished - 23 Aug 2020
Externally publishedYes
Event16th European Conference on Computer Vision: ECCV 2020 - Virtual, Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12348 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th European Conference on Computer Vision
Abbreviated titleECCV 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period23/08/2028/08/20

Keywords

  • Classification
  • Feature matching
  • Visual localization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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