Estimation of the fundamental matrix is key to many problems in computer vision as it allows recovery of the epipolar geometry between camera images of the same scene. The estimation from feature correspondences has been widely addressed in the literature, particularly in the presence of outliers. In this paper, we propose a new robust method to estimate the fundamental matrix from two sets of features without any correspondence information. The method operates in the frequency domain and the underlying estimation process considers all features simultaneously, thus yielding a high robustness with respect to noise and outliers. In addition, we show that the method is well-suited to widely separate viewpoints
