Generic Camera Calibration using Blurry Images
This work aims to improve the accuracy of generic camera calibration for individual users by making it more robust to motion blur, a common practical issue.
This paper addresses the challenge of generic camera calibration, which typically requires many images and is susceptible to motion blur. The authors propose a method that simultaneously estimates feature locations and spatially varying point spread functions, while resolving translational ambiguity, to enable effective calibration using blurry images.
Camera calibration is the foundation of 3D vision. Generic camera calibration can yield more accurate results than parametric cam era calibration. However, calibrating a generic camera model using printed calibration boards requires far more images than parametric calibration, making motion blur practically unavoidable for individual users. As a f irst attempt to address this problem, we draw on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks. Experimental results validate the effectiveness of our approach.