🤖 AI Summary
This work proposes a novel approach to generic camera calibration under motion blur, a condition that typically degrades accuracy due to reliance on sharp images. The method jointly estimates feature point locations and spatially varying point spread functions (PSFs) directly from blurred images—marking the first integration of blurred feature localization and PSF estimation within generic camera calibration. By incorporating geometric constraints and a locally parameterized illumination model, the approach effectively models translational blur and resolves the ambiguities it introduces. Experimental results demonstrate that the proposed technique achieves high-precision calibration even in the presence of significant motion blur, thereby extending the applicability of generic calibration methods to more challenging real-world imaging conditions.
📝 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.