  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 comprehensive survey of learning-based camera calibration techniques,
by analyzing their strengths and limitations. Our main calibration categories
include the standard pinhole camera model, distortion camera model, cross-view
model, and cross-sensor model, following the research trend and extended
applications. As there is no benchmark in this community, we collect a holistic
calibration dataset that can serve as a public platform to evaluate the
generalization of existing methods. It comprises both synthetic and real-world
data, with images and videos captured by different cameras in diverse scenes.
Toward the end of this paper, we discuss the challenges and provide further
research directions. To our knowledge, this is the first survey for the
learning-based camera calibration (spanned 8 years). The summarized methods,
datasets, and benchmarks are available and will be regularly updated at
https://github.com/KangLiao929/Awesome-Deep-Camera-Calibration.
