BabelCalib: A Universal Approach to Calibrating Central Cameras
- URL: http://arxiv.org/abs/2109.09704v1
- Date: Mon, 20 Sep 2021 17:21:57 GMT
- Title: BabelCalib: A Universal Approach to Calibrating Central Cameras
- Authors: Yaroslava Lochman, Kostiantyn Liepieshov, Jianhui Chen, Michal
Perdoch, Christopher Zach, James Pritts
- Abstract summary: Existing calibration methods occasionally fail for large field-of-view cameras.
We propose 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.
- Score: 24.662262051346087
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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