@article{
 title = {S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching},
 type = {article},
 year = {2020},
 keywords = {classification,feature matching,visual localization},
 pages = {11-13},
 websites = {http://arxiv.org/abs/2004.01673},
 id = {c18e7e69-95fb-3cfe-a6bf-5b6261bf2947},
 created = {2022-09-19T09:01:25.013Z},
 file_attached = {true},
 profile_id = {276016a7-2c9d-3507-8888-093db7c54774},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2022-09-22T17:08:08.907Z},
 read = {true},
 starred = {true},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {02fb5526-03ff-44ad-8d5c-42bd496c3100,1608176d-7332-416b-a03b-89c3f2ae3ab6},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Germain, Hugo and Bourmaud, Guillaume and Lepetit, Vincent}
}

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