  Self-calibration of camera intrinsics and radial distortion has a long
history of research in the computer vision community. However, it remains rare
to see real applications of such techniques to modern Simultaneous Localization
And Mapping (SLAM) systems, especially in driving scenarios. In this paper, we
revisit the geometric approach to this problem, and provide a theoretical proof
that explicitly shows the ambiguity between radial distortion and scene depth
when two-view geometry is used to self-calibrate the radial distortion. In view
of such geometric degeneracy, we propose a learning approach that trains a
convolutional neural network (CNN) on a large amount of synthetic data. We
demonstrate the utility of our proposed method by applying it as a
checkerboard-free calibration tool for SLAM, achieving comparable or superior
performance to previous learning and hand-crafted methods.
