Generic Camera Calibration using Blurry Images

TL;DR

This paper introduces a novel method for generic camera calibration that effectively handles blurry images by estimating feature locations and spatially varying point spread functions, improving calibration accuracy without requiring clear images.

Contribution

It presents the first approach to calibrate generic cameras using blurry images by jointly estimating features and blur, addressing a key challenge in practical camera calibration.

Findings

Effective calibration with blurry images demonstrated

Joint estimation improves accuracy over traditional methods

Experimental validation confirms robustness of the approach

Abstract

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.