cameracalibration

Camera calibration is the process of determining the intrinsic and extrinsic parameters of a camera. Intrinsic parameters describe the camera's internal properties, such as focal length, principal point, and lens distortion. Extrinsic parameters describe the camera's position and orientation in 3D space relative to a world coordinate system. Accurate camera calibration is crucial for many computer vision applications, including 3D reconstruction, augmented reality, and object tracking.

There are several methods for camera calibration. One common approach involves using a known calibration pattern, such as a chessboard or a circle grid. By detecting the pattern in multiple images taken from different viewpoints, the intrinsic and extrinsic parameters can be estimated through optimization algorithms. The accuracy of calibration depends on the quality of the images, the precision of the pattern detection, and the suitability of the chosen calibration model.

The intrinsic parameters are often represented by a camera matrix, which relates pixel coordinates to normalized image coordinates. Lens distortion, such as radial and tangential distortion, is also modeled and corrected during calibration. The extrinsic parameters are typically represented by a rotation matrix and a translation vector, defining the camera's pose. Once calibrated, these parameters allow for the transformation of points between the 3D world and the 2D image plane, and vice versa. This enables tasks like accurately measuring distances, reconstructing 3D scenes, and overlaying virtual objects onto real-world views.