TRPLP – Trifocal Relative Pose From Lines at Points

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00347781" target="_blank" >RIV/68407700:21730/20:00347781 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/CVPR42600.2020.01209" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.01209</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR42600.2020.01209" target="_blank" >10.1109/CVPR42600.2020.01209</a>

Jazyk výsledku

angličtina

Název v původním jazyce

TRPLP – Trifocal Relative Pose From Lines at Points

Popis výsledku v původním jazyce

We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of (i) three points and one line and (ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Grobner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We show in simulated experiments that our solvers are numerically robust and stable under image noise. We show in real experiment that (i) SIFT features provide good enough point-and-line correspondences for three-view reconstruction and (ii) that we can solve difficult cases with too few or too noisy tentative matches where the state of the art structure from motion initialization fails.

Název v anglickém jazyce

TRPLP – Trifocal Relative Pose From Lines at Points

Popis výsledku anglicky

We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of (i) three points and one line and (ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Grobner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We show in simulated experiments that our solvers are numerically robust and stable under image noise. We show in real experiment that (i) SIFT features provide good enough point-and-line correspondences for three-view reconstruction and (ii) that we can solve difficult cases with too few or too noisy tentative matches where the state of the art structure from motion initialization fails.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

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

978-1-7281-7168-5

ISSN

1063-6919

e-ISSN

2575-7075

Počet stran výsledku

11

Strana od-do

12070-12080

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

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