Weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects.

In this paper, a weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects is proposed. First, we propose a new estimation method for computing the relative depth of the free endpoint on the 1D object and prove its robustness against noise compared with those used in previous literature. The introduced estimator is invariant to image similarity transforms, resulting in a similarity-invariant linear calibration algorithm which is slightly more accurate than the well-known normalized linear algorithm.