TRPLP – Trifocal Relative Pose From Lines at Points
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
TRPLP – Trifocal Relative Pose From Lines at Points
Original language description
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.
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
978-1-7281-7168-5
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
11
Pages from-to
12070-12080
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
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