MAGSAC: Marginalizing Sample Consensus

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F19%3A00337151" target="_blank" >RIV/68407700:21110/19:00337151 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/19:00337151

Result on the web

<a href="https://ieeexplore.ieee.org/document/8953287" target="_blank" >https://ieeexplore.ieee.org/document/8953287</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2019.01044" target="_blank" >10.1109/CVPR.2019.01044</a>

Result language

angličtina

Original language name

MAGSAC: Marginalizing Sample Consensus

Original language description

A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating the noise sigma, it is marginalized over a range of noise scales. The optimized model is obtained by weighted least-squares fitting where the weights come from the marginalization over sigma of the point likelihoods of being inliers. A new quality function is proposed not requiring sigma and, thus, a set of inliers to determine the model quality. Also, a new termination criterion for RANSAC is built on the proposed marginalization approach. Applying sigma-consensus, MAGSAC is proposed with no need for a user-defined sigma and improving the accuracy of robust estimation significantly. It is superior to the state-of-the-art in terms of geometric accuracy on publicly available real-world datasets for epipolar geometry (F and E) and homography estimation. In addition, applying sigma-consensus only once as a post-processing step to the RANSAC output always improved the model quality on a wide range of vision problems without noticeable deterioration in processing time, adding a few milliseconds.

Czech name

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Czech description

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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)

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)

Publication year

2019

Confidentiality

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

Article name in the collection

CVPR 2019: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition

ISBN

978-1-7281-3293-8

ISSN

1063-6919

e-ISSN

2575-7075

Number of pages

9

Pages from-to

10189-10197

Publisher name

IEEE

Place of publication

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Event location

Long Beach

Event date

Jun 15, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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