Iterative Multi-Planar Camera Calibration: Improving Stability using Model Selection

Abstract

Tracking, or camera pose determination, is the main technical challenge in numerous applications in computer vision and especially in Augmented Reality. However, pose computation processes commonly exhibit some fluctuations and lack of precision in the estimation of the parameters. This leads to unpleasant visual impressions when augmented scenes are considered. In this paper, we propose an efficient and reliable method for real time camera tracking which avoid unpleasant statistical fluctuations. This method is based on the knowledge of a piecewise planar structure in the scene and makes use of model selection to reduce fluctuations. Videos are attached to this paper which prove the effectiveness of our approach.