Separable Four Points Fundamental Matrix
This is an incremental improvement for computer vision applications requiring efficient fundamental matrix estimation.
The paper tackles the problem of RANSAC-based fundamental matrix computation by introducing a method based on epipolar homography decomposition, which guarantees a minimal number of evaluated hypotheses when four correspondences lie on an image line, providing fast and accurate results on real-world image pairs.
We present a novel approach for RANSAC-based computation of the fundamental matrix based on epipolar homography decomposition. We analyze the geometrical meaning of the decomposition-based representation and show that it directly induces a consecutive sampling strategy of two independent sets of correspondences. We show that our method guarantees a minimal number of evaluated hypotheses with respect to current minimal approaches, on the condition that there are four correspondences on an image line. We validate our approach on real-world image pairs, providing fast and accurate results.