MAGSAC++, a Fast, Reliable and Accurate Robust Estimator

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21230/20:00346345

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

MAGSAC++, a Fast, Reliable and Accurate Robust Estimator

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

MAGSAC++, a Fast, Reliable and Accurate Robust Estimator

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition

ISBN

978-1-7281-7169-2

ISSN

1063-6919

e-ISSN

2575-7075

Počet stran výsledku

9

Strana od-do

1301-1309

Název nakladatele

IEEE Computer Society

Místo vydání

USA

Místo konání akce

Seattle

Datum konání akce

13. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000620679501055