Radial Distortion Triangulation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335893" target="_blank" >RIV/68407700:21230/19:00335893 - isvavai.cz</a>
Result on the web
<a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Kukelova_Radial_Distortion_Triangulation_CVPR_2019_paper.html" target="_blank" >http://openaccess.thecvf.com/content_CVPR_2019/html/Kukelova_Radial_Distortion_Triangulation_CVPR_2019_paper.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR.2019.00991" target="_blank" >10.1109/CVPR.2019.00991</a>
Alternative languages
Result language
angličtina
Original language name
Radial Distortion Triangulation
Original language description
This paper presents the first optimal, maximal likelihood, solution to the triangulation problem for radially distorted cameras. The proposed solution to the two-view triangulation problem minimizes the L2-norm of the reprojection error in the distorted image space. We cast the problem as the search for corrected distorted image points, and we use a Lagrange multiplier formulation to impose the epipolar constraint for undistorted points. For the one-parameter division model, this formulation leads to a system of five quartic polynomial equations in five unknowns, which can be exactly solved using the Groebner basis method. While the proposed Groebner basis solution is provably optimal; it is too slow for practical applications. Therefore, we developed a fast iterative solver to this problem. Extensive empirical tests show that the iterative algorithm delivers the optimal solution virtually every time, thus making it an L2-optimal algorithm de facto. It is iterative in nature, yet in practice, it converges in no more than five iterations. We thoroughly evaluate the proposed method on both synthetic and real-world data, and we show the benefits of performing the triangulation in the distorted space in the presence of radial distortion.
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
<a href="/en/project/EF17_050%2F0008025" target="_blank" >EF17_050/0008025: International Mobility of Researchers ? MSCA-IF in CTU</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
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
9673-9681
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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