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

Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion