MAGSAC++, a Fast, Reliable and Accurate Robust Estimator
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F20%3A00346345" target="_blank" >RIV/68407700:21110/20:00346345 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/20:00346345
Result on the web
<a href="https://doi.org/10.1109/CVPR42600.2020.00138" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.00138</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR42600.2020.00138" target="_blank" >10.1109/CVPR42600.2020.00138</a>
Alternative languages
Result language
angličtina
Original language name
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator
Original language description
A new method for robust estimation, MAGSAC++ , is proposed. It introduces a new model quality (scoring) function that does not require the inlier-outlier decision, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. We also propose a new sampler, Progressive NAPSAC, for RANSAC-like robust estimators. Exploiting the fact that nearby points often originate from the same model in real-world data, it finds local structures earlier than global samplers. The progressive transition from local to global sampling does not suffer from the weaknesses of purely localized samplers. On six publicly available realworld datasets for homography and fundamental matrix fitting, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is faster, more geometrically accurate and fails less often.
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
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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
ISBN
978-1-7281-7169-2
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
9
Pages from-to
1301-1309
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Seattle
Event date
Jun 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000620679501055