  Although recent learning-based calibration methods can predict extrinsic and
intrinsic camera parameters from a single image, the accuracy of these methods
is degraded in fisheye images. This degradation is caused by mismatching
between the actual projection and expected projection. To address this problem,
we propose a generic camera model that has the potential to address various
types of distortion. Our generic camera model is utilized for learning-based
methods through a closed-form numerical calculation of the camera projection.
Simultaneously to recover rotation and fisheye distortion, we propose a
learning-based calibration method that uses the camera model. Furthermore, we
propose a loss function that alleviates the bias of the magnitude of errors for
four extrinsic and intrinsic camera parameters. Extensive experiments
demonstrated that our proposed method outperformed conventional methods on two
largescale datasets and images captured by off-the-shelf fisheye cameras.
Moreover, we are the first researchers to analyze the performance of
learning-based methods using various types of projection for off-the-shelf
cameras.
