Rank-Constrained Fundamental Matrix Estimation by Polynomial Global Optimization Versus the Eight-Point Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00230532" target="_blank" >RIV/68407700:21230/15:00230532 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10851-014-0545-9" target="_blank" >http://dx.doi.org/10.1007/s10851-014-0545-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10851-014-0545-9" target="_blank" >10.1007/s10851-014-0545-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rank-Constrained Fundamental Matrix Estimation by Polynomial Global Optimization Versus the Eight-Point Algorithm

Popis výsledku v původním jazyce

The fundamental matrix can be estimated from point matches. The current gold standard is to bootstrap the eight-point algorithm and two-viewprojective bundle adjustment. The eight-point algorithm first computes a simple linear least squares solution by minimizing an algebraic cost and then projects the result to the closest rank-deficient matrix. We propose a single-step method that solves both steps of the eight-point algorithm. Using recent results from polynomial global optimization, our method findsthe rank-deficient matrix that exactly minimizes the algebraic cost. In this special case, the optimizationmethod is reduced to the resolution of very short sequences of convex linear problems which are computationally efficient and numerically stable.The current gold standard is known to be extremely effective but is nonetheless outperformed by our rank-constrained method for bootstrapping bundle adjustment. This is here demonstrated on simulated and standard real datasets.With our in

Název v anglickém jazyce

Rank-Constrained Fundamental Matrix Estimation by Polynomial Global Optimization Versus the Eight-Point Algorithm

Popis výsledku anglicky

The fundamental matrix can be estimated from point matches. The current gold standard is to bootstrap the eight-point algorithm and two-viewprojective bundle adjustment. The eight-point algorithm first computes a simple linear least squares solution by minimizing an algebraic cost and then projects the result to the closest rank-deficient matrix. We propose a single-step method that solves both steps of the eight-point algorithm. Using recent results from polynomial global optimization, our method findsthe rank-deficient matrix that exactly minimizes the algebraic cost. In this special case, the optimizationmethod is reduced to the resolution of very short sequences of convex linear problems which are computationally efficient and numerically stable.The current gold standard is known to be extremely effective but is nonetheless outperformed by our rank-constrained method for bootstrapping bundle adjustment. This is here demonstrated on simulated and standard real datasets.With our in

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Journal of Mathematical Imaging and Vision

ISSN

0924-9907

e-ISSN

—

Svazek periodika

53

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

42-60

Kód UT WoS článku

000357289700004

EID výsledku v databázi Scopus

2-s2.0-84934439848