Robust Self-calibration of Focal Lengths from the Fundamental Matrix

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380113" target="_blank" >RIV/68407700:21230/24:00380113 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/CVPR52733.2024.00499" target="_blank" >https://doi.org/10.1109/CVPR52733.2024.00499</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR52733.2024.00499" target="_blank" >10.1109/CVPR52733.2024.00499</a>

Result language

angličtina

Original language name

Robust Self-calibration of Focal Lengths from the Fundamental Matrix

Original language description

The problem of self-calibration of two cameras from a given fundamental matrix is one of the basic problems in geometric computer vision. Under the assumption of known principal points and square pixels, the Bougnoux formula offers a means to compute the two unknown focal lengths. However, in many practical situations, the formula yields inaccurate results due to commonly occurring singularities. Moreover, the estimates are sensitive to noise in the com-puted fundamental matrix and to the assumed positions of the principal points. In this paper, we therefore propose an efficient and robust iterative method to estimate the focal lengths along with the principal points of the cameras given a fundamental matrix and priors for the estimated camera intrinsics. In addition, we study a computationally efficient check of models generated within RANSAC that improves the accuracy of the estimated models while reducing the to-tal computational time. Extensive experiments on real and synthetic data show that our iterative method brings signifi-cant improvements in terms of the accuracy of the estimated focal lengths over the Bougnoux formula and other state-of-the-art methods, even when relying on inaccurate priors. The code for the methods and experiments is available at https://github.com/kocurvik/robust.self.calibration

Czech name

—

Czech description

—

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)

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)

Publication year

2024

Confidentiality

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

Article name in the collection

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

ISBN

979-8-3503-5300-6

ISSN

1063-6919

e-ISSN

2575-7075

Number of pages

10

Pages from-to

5220-5229

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

001322555905059