Visualizing and Alleviating the Effect of Radial Distortion on Camera Calibration Using Principal Lines
This work addresses camera calibration challenges for computer vision applications, but it is incremental as it builds on existing principal line methods.
The paper tackles the problem of radial distortion affecting camera calibration accuracy by proposing new data preparation guidelines using principal lines, resulting in more robust and consistent calibration compared to existing algebraic methods.
Preparing appropriate images for camera calibration is crucial to obtain accurate results. In this paper, new suggestions for preparing such data to alleviate the adverse effect of radial distortion for a calibration procedure using principal lines are developed through the investigations of: (i) identifying directions of checkerboard movements in an image which will result in maximum (and minimum) influence on the calibration results, and (ii) inspecting symmetry and monotonicity of such effect in (i) using the above principal lines. Accordingly, it is suggested that the estimation of principal point should based on linearly independent pairs of nearly parallel principal lines, with a member in each pair corresponds to a near 180-degree rotation (in the image plane) of the other. Experimental results show that more robust and consistent calibration results for the foregoing estimation can actually be obtained, compared with the renowned algebraic methods which estimate distortion parameters explicitly.