BabelCalib: A Universal Approach to Calibrating Central Cameras
Paper i proceeding, 2021

Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parameters of the used camera model. This might occur because a simpler projection model is assumed in an initial step, or a poor initial guess for the internal parameters is pre-defined. A lot of the difficulties of general camera calibration lie in the use of a forward projection model. We side-step these challenges by first proposing a solver to calibrate the parameters in terms of a back-projection model and then regress the parameters for a target forward model. These steps are incorporated in a robust estimation framework to cope with outlying detections. Extensive experiments demonstrate that our approach is very reliable and returns the most accurate calibration parameters as measured on the downstream task of absolute pose estimation on test sets. The code is released at https://github.com/ylochman/babelcalib.

minimal solvers

camera calibration

fisheye calibration

Författare

Yaroslava Lochman

Datorseende och medicinsk bildanalys

Kostiantyn Liepieshov

Ukrainian Catholic University

Jianhui Chen

Facebook Reality Labs

Michal Perdoch

Facebook Reality Labs

Christopher Zach

Datorseende och medicinsk bildanalys

James Pritts

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Proceedings of the IEEE International Conference on Computer Vision

15505499 (ISSN)

15233-15242
978-1-6654-2813-2 (ISBN)

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Montreal, Canada,

Ämneskategorier

Annan data- och informationsvetenskap

Datorseende och robotik (autonoma system)

Styrkeområden

Informations- och kommunikationsteknik

DOI

10.1109/ICCV48922.2021.01497

Mer information

Senast uppdaterat

2022-04-28