Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362942" target="_blank" >RIV/68407700:21230/22:00362942 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TPAMI.2021.3071812" target="_blank" >https://doi.org/10.1109/TPAMI.2021.3071812</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2021.3071812" target="_blank" >10.1109/TPAMI.2021.3071812</a>
Alternative languages
Result language
angličtina
Original language name
Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures
Original language description
We propose Graph-Cut RANSAC, GC-RANSAC in short, a new robust geometric model estimation method where the local optimization step is formulated as energy minimization with binary labeling, applying the graph-cut algorithm to select inliers. The minimized energy reflects the assumption that geometric data often form spatially coherent structures - it includes both a unary component representing point-to-model residuals and a binary term promoting spatially coherent inlier-outlier labelling of neighboring points. The proposed local optimization step is conceptually simple, easy to implement, efficient with a globally optimal inlier selection given the model parameters. Graph-Cut RANSAC, equipped with "the bells and whistles" of USAC and MAGSAC++, was tested on a range of problems using a number of publicly available datasets for homography, 6D object pose, fundamental and essential matrix estimation. It is more geometrically accurate than state-of-the-art robust estimators, fails less often and runs faster or with speed similar to less accurate alternatives. The source code is available at https://github.com/danini/graph-cut-ransac.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
1939-3539
Volume of the periodical
44
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
4961-4974
UT code for WoS article
000836666600036
EID of the result in the Scopus database
2-s2.0-85104195335