Get Geometric Calibration For Mobile, Stereo, Autofocus Cameras

Stereo calibration method by finding a planar homography relationship between wide and narrow-angle camera images. Four points from the inner pattern in both camera images are used to calculate the homography transformation.

Depth resolution depends on accurate disparity estimations. The accuracy of disparity estimation is directly proportional to pixel size, where smaller pixel sizes provide better resolution. Typically, disparity can be estimated accurately to about one fifth of a pixel.

Stereo calibration computes the essential matrix (E) and the fundamental matrix (F). The essential matrix (E) contains the rotation and translation information required to relate the location of a point (Pl) as seen by the left camera to the same point (Pr) as seen by the right camera, in real-world coordinates.

Geometric calibration refers to the ability of a camera to translate a 2D image into a 3D object to map the real-world field of view. In photogrammetry, distances are measured in images. In conventional photography, distortion is measured and compensated in the image processing pipe.

Camera auto-calibration is the process of determining internal camera parameters directly from multiple uncalibrated images of unstructured scenes. In contrast to classic camera calibration, auto-calibration does not require any special calibration objects in the scene.

Follow this workflow to calibrate your stereo camera using the app: Prepare images, camera, and calibration pattern. Add image pairs. Calibrate the stereo camera. Evaluate calibration accuracy. Adjust parameters to improve accuracy (if necessary). Export the parameters object.

Generally, the camera calibration process uses images of a 3D object with a geometrical pattern (e.g. checker board). The pattern is called the calibration grid. The 3D co-ordinates of the pattern are matched to 2D image points. The correspondences are used to solve the equation system in Eq.