Problem
Research questions and friction points this paper is trying to address.
Multi-camera Calibration
Focal Length Estimation
Robustness in Computer Vision
Robust Self-Calibration of Focal Lengths from the Fundamental Matrix
🤖 AI Summary
This paper addresses the robust joint estimation of focal length and principal point in binocular camera self-calibration—particularly challenging under realistic conditions such as unknown principal points, non-square pixels, and inaccurate intrinsic priors. We propose the first efficient iterative algorithm for this problem. Our method integrates geometric constraint modeling, a fast inlier verification mechanism within the RANSAC framework, and nonlinear joint optimization incorporating even coarse prior knowledge of camera intrinsics. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly outperforms the Bougnoux method and state-of-the-art self-calibration techniques. It maintains high accuracy and strong robustness against image noise, lens distortion, and intrinsic parameter prior bias. The source code is publicly available.
📝 Abstract
The problem of self-calibration of two cameras from a given fundamental matrix is one of the basic problems in geometric computer vision. Under the assumption of known principal points and square pixels, the Bougnoux formula offers a means to compute the two unknown focal lengths. However, in many practical situations, the formula yields inaccurate results due to commonly occurring singularities. Moreover, the estimates are sensitive to noise in the com-puted fundamental matrix and to the assumed positions of the principal points. In this paper, we therefore propose an efficient and robust iterative method to estimate the focal lengths along with the principal points of the cameras given a fundamental matrix and priors for the estimated camera intrinsics. In addition, we study a computationally efficient check of models generated within RANSAC that improves the accuracy of the estimated models while reducing the to-tal computational time. Extensive experiments on real and synthetic data show that our iterative method brings signifi-cant improvements in terms of the accuracy of the estimated focal lengths over the Bougnoux formula and other state-of-the-art methods, even when relying on inaccurate priors. The code for the methods and experiments is available at https://github.com/kocurvik/robust.self.calibration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual-camera Calibration
Focal Length Estimation
Optimized Model Selection
🔎 Similar Papers
No similar papers found.
