Global Focal and Radial Distortion Averaging from Radial Fundamental Matrices for Robust Self-Calibration

Classical self-calibration techniques either perform computationally expensive bundle adjustment to estimate all camera parameters or initialize the focal lengths alone by globally averaging fundamental matrices, typically ignoring radial distortion. Both strategies can degrade accuracy in large-scale Structure from Motion pipelines.We present a calibration method that overcomes these limitations by averaging focal lengths and radial distortion parameters using radial fundamental matrices. This method avoids costly point-wise optimization. Our algorithm minimizes the geometric distance between an observed fundamental matrix and the essential-matrix manifold. This provides a mathematically consistent and highly scalable framework for calibrating the camera's intrinsic parameters.Experiments on diverse real-world datasets demonstrate that our joint estimator provides more precise focal length and distortion parameter estimates than existing methods. Furthermore, we demonstrate that naive, independent distortion averaging is suboptimal, which reinforces the importance of joint focal-radial estimation. These results underscore the importance of incorporating radial distortion averaging into modern self-calibration methods to improve reconstruction accuracy and stability.