Camera calibration from very few images based on soft constraint optimization

Camera calibration is a basic and crucial problem in photogrammetry and computer vision. Although existing calibration techniques exhibit excellent precision and flexibility in classical cases, most of them need from 5 to 10 calibration images. Unfortunately, only a limited number of calibration images and control points can be available in many application fields such as criminal investigation, industrial robot and augmented reality. For these cases, this paper presented a two-step calibration based on soft constraint optimization, which is motivated by "no free lunch" theorem and error analysis. The key steps include (1) homography estimation with weighting function, (2) Initialization based on a simplified model, and (3) soft constraint optimization in terms of reprojection error. The proposed method provides direct access to geometric information of the object from very few images. After extensive experiments, the results demonstrate that the proposed algorithm outperforms Zhang's algorithms from the point of view of the success ratio, accuracy and precision.