Publication
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, USA, IEEE, 2020.
Abstract
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.