Noisy One-point Homographies are Surprisingly Good
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380042" target="_blank" >RIV/68407700:21230/24:00380042 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR52733.2024.00490" target="_blank" >https://doi.org/10.1109/CVPR52733.2024.00490</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52733.2024.00490" target="_blank" >10.1109/CVPR52733.2024.00490</a>
Alternative languages
Result language
angličtina
Original language name
Noisy One-point Homographies are Surprisingly Good
Original language description
Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple, the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches, each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice, this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size, from four point correspondences originally, by including additional information such as keypoint orientation/angles or local affine information. In this work, we continue in this direction and propose a novel one-point solver that leverages different approximate constraints derived from the same auxiliary information. In experiments we obtain state-of-the-art results, with execution time speed-ups, on large benchmark datasets and show that it is more beneficial for the solver to be sample efficient compared to generating more accurate homographies.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
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/GM22-23183M" target="_blank" >GM22-23183M: New generation of camera geometry solvers</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
979-8-3503-5301-3
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
5125-5134
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Seattle
Event date
Jun 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001322555905050