Camera calibration involves estimating camera parameters to infer geometric features from
captured sequences, which is crucial for computer vision and robotics. However,
conventional calibration is laborious and requires dedicated collection. Recent efforts show
that learning-based solutions have the potential to be used in place of the repeatability
works of manual calibrations. Among these solutions, various learning strategies, networks,
geometric priors, and datasets have been investigated. In this paper, we provide a�…