A robust and accurate self-calibration approach from unordered wide-baseline images

This paper presents a robust and accurate self-calibration approach from unordered wide-baseline images. An optimization model based on affine transformation is introduced into propagation to acquire higher accuracy of quasi-dense correspondences. Our self-calibration algorithm is completed through two-layer iteration. In the inner layer, global objective function and local photometric consistency is used to iteratively optimize camera parameters and scene structure. In the outer layer, a resampling strategy is presented to iteratively select a group of quasi-dense correspondences to solve the camera parameters and scene structure. We demonstrate our algorithm with several high accurate calibration results.