Camera calibration is essential in photogrammetry and computer vision. Herein, the application and classification of camera calibration are first introduced. Subsequently, the theoretical basis of calibration is summarized, including spatial coordinate system transformation, geometric imaging model, internal and external parameter calculation methods, and camera calibration methods described based on classical and intelligent aspects. Conventional calibration methods include reference object-based, active vision, and self-calibration methods. Then, a comprehensive analysis of their advantages and disadvantages is provided. Meanwhile, in intelligent calibration, error backpropagation, multilayer perceptrons, and convolution neural networks are involved. The typical indexes used to evaluate camera calibration methods are summarized. Finally, a summary is provided, and the development direction of camera calibration technology is discussed, which can provide a reference for researchers investigating camera calibration.