BabelCalib: A Universal Approach to Calibrating Central Cameras

Yaroslava Lochman, Kostiantyn Liepieshov, Jianhui Chen, Michal Perdoch, Christopher Zach, James Pritts; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15253-15262


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

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@InProceedings{Lochman_2021_ICCV, author = {Lochman, Yaroslava and Liepieshov, Kostiantyn and Chen, Jianhui and Perdoch, Michal and Zach, Christopher and Pritts, James}, title = {BabelCalib: A Universal Approach to Calibrating Central Cameras}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {15253-15262} }