BabelCalib: A Universal Approach to Calibrating Central Cameras

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 of absolute pose estimation on test sets. The code is released at https://github.com/ylochman/babelcalib.