BabelCalib: A Universal Approach to Calibrating Central Cameras
Yaroslava Lochman, Kostiantyn Liepieshov, Jianhui Chen, Michal, Perdoch, Christopher Zach, James Pritts

TL;DR
BabelCalib introduces a robust, universal calibration method for central cameras that overcomes traditional non-linearity issues by using a back-projection model and regression, achieving high accuracy in pose estimation tasks.
Contribution
The paper presents a novel two-step calibration approach that sidesteps non-linearity challenges by combining back-projection calibration with parameter regression within a robust framework.
Findings
High calibration accuracy demonstrated on test sets
Reliable performance across various camera models
Improved pose estimation results
Abstract
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…
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Taxonomy
TopicsAdvanced Vision and Imaging · Optical measurement and interference techniques · Robotics and Sensor-Based Localization
MethodsTest
