用模糊图像实现通用相机标定，无需清晰图像。
Generic Camera Calibration using Blurry Images
- 结合几何约束与局部光照模型，同步估计特征点与模糊核。
- 在仅含模糊图像条件下完成标定，避免传统方法需大量图像的缺陷。
- 适合普通用户在运动模糊场景下进行相机标定。
相机标定是3D视觉的基础。通用相机标定相比参数化标定能获得更精确的结果，但使用打印标定板进行通用标定时所需图像数量远超参数化方法，导致个体用户难以避免运动模糊。本文首次尝试解决此问题，利用几何约束和局部参数化光照模型，同时估计特征位置与空间变化的点扩散函数，并解决传统图像去模糊任务中无需考虑的平移模糊歧义性。实验结果验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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