Generic Camera 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.

Accurate Checkerboard Corner Detection under Defoucs

Camera calibration is a critical process in 3D vision, im pacting applications in autonomous driving, robotics, ar chitecture, and so on. This paper focuses on enhancing feature extraction for chessboard corner detection, a key step in calibration. We analyze existing methods, high lighting their limitations and propose a novel sub-pixel refinement approach based on symmetry, which signifi cantly improves accuracy for visible light cameras. Un like prior symmetry based method that assume a contin uous physical pattern, our approach accounts for abrupt changes in visible light camera images and defocus ef fects. We introduce a simplified objective function that reduces computation time and mitigates overfitting risks. Furthermore, we derive an explicit expression for the pixel value of a blurred edge, providing insights into the relationship between pixel value and center intensity. Our method demonstrates superior performance, achiev ing substantial accuracy improvements over existing tech niques, particularly in the context of visible light cam era calibration. Our code is available from https: //github.com/spdfghi/Accurate-Checkerboard Corner-Detection-under-Defoucs.git.