Separable Four Points Fundamental Matrix

Gil Ben-Artzi
Ariel University, Israel

In WACV 2021


Python Code (Prototype Only)

C++ Code (Fast)



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.

RANSAC Iterations vs. Outlier Rate

                      Ideal Case: one solution per sample

              Practical Case: 2.43 solutions per sample,
                71 perprocessing iterations for our approach

Our Two-Steps Approach - How it works

A. Efficiently match a line segment with at least 4 points across images

B. Compute epipolar homography, sample 4 more points and compute F

Try our code!


Separable Four Points Fundamental Matrix, G. Ben-Artzi


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