StereoGlue: Robust Estimation with Single-Point Solvers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00380154" target="_blank" >RIV/68407700:21230/25:00380154 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-72998-0_24" target="_blank" >https://doi.org/10.1007/978-3-031-72998-0_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-72998-0_24" target="_blank" >10.1007/978-3-031-72998-0_24</a>
Alternative languages
Result language
angličtina
Original language name
StereoGlue: Robust Estimation with Single-Point Solvers
Original language description
We propose StereoGlue, a method designed for joint feature matching and robust estimation that effectively reduces the combinatorial complexity of these tasks using single-point minimal solvers. StereoGlue is applicable to a range of problems, including but not limited to relative pose and homography estimation, determining absolute pose with 2D-3D correspondences, and estimating 3D rigid transformations between point clouds. StereoGlue starts with a set of one-to-many tentative correspondences, iteratively forms tentative matches, and estimates the minimal sample model. This model then facilitates guided matching, leading to consistent one-to-one matches, whose number serves as the model score. StereoGlue is superior to the state-of-the-art robust estimators on real-world datasets on multiple problems, improving upon a number of recent feature detectors and matchers. Additionally, it shows improvements in point cloud matching and absolute camera pose estimation. The code is at: https://github.com/danini/stereoglue.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision – ECCV 2024, Part LVII
ISBN
978-3-031-72997-3
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
21
Pages from-to
421-441
Publisher name
Springer, Cham
Place of publication
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Event location
Milano
Event date
Sep 29, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001346379600024