S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching

• URL: http://arxiv.org/abs/2004.01673v1
• Date: Fri, 3 Apr 2020 17:04:34 GMT
• Title: S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching
• Authors: Hugo Germain, Guillaume Bourmaud, Vincent Lepetit
• Abstract summary: S2DNet is a novel feature matching pipeline designed and trained to efficiently establish robust and accurate correspondences. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-term visual localization datasets.
• Score: 36.48376198922595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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