Stable Intrinsic Auto-Calibration from Fundamental Matrices of Devices with Uncorrelated Camera Parameters

Torben Fetzer, Gerd Reis, Didier Stricker

In: 2020 IEE Winter Conference on Applications of Computer Vision |. IEEE Winter Conference on Applications of Computer Vision (WACV-2020) Aspen CO United States IEEE 2020.


Auto-Calibration is an important task in computer vision and is necessary for many visual applications. Methods like photogrammetry, depth map estimation, metrology, augmented/mixed reality or odometry are strongly dependent on well calibrated devices. While classical calibration relies on tools like checkerboards or additional scene information, auto-calibration only takes epipolar relations into account. Classical calibration is often impractical, tends to de-adjust over time and distributes the error over the entire, limited working volume. Auto-calibration, on the other hand, does not require any information other than the image content itself, has a virtually unlimited working range and usually achieves highest accuracy at the objects’ surfaces. Unfortunately, auto-calibration methods are sensitive to errors in the fundamental matrix and need good initialization to converge to the global solution. In practice this leads to difficulties if optical parameters like principal point or focal length are unconstrained. In such situations, even state-ofthe- art auto-calibration methods tend to diverge and do not yield a valid calibration. This work assesses reasons for this behavior, in particular for the initialization method of Bougnoux [3] and Lourakis’ state-of-the-art auto-calibration method [21]. Based on the analysis, a more stable method is proposed. A continuous and smooth energy functional is introduced, providing superior convergence properties. I.e. it can not diverge, converges faster, and has a significantly enlarged convergence region with respect to the global minimum. Finally, a thorough evaluation has been conducted and a detailed comparison with the state of the art is presented.


StableIntrinsicAutoCalibration.pdf (pdf, 622 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz