Separable Four Points Fundamental Matrix

Gil Ben-Artzi
Ariel University, Israel





We present an approach for the computation of the fundamental matrix based on epipolar homography decomposition. We analyze the geometrical meaning of the decomposition-based representation and show that it guarantees a minimal number of RANSAC samples, on the condition that four correspondences are on an image line. Experiments on real-world image pairs show that our approach successfully recovers such four correspondences, provides accurate results and requires only a small number of RANSAC iterations.

RANSAC Iterations vs. Outlier Rate

                      Ideal Case: one solution per sample

              Practical Case: 2.43 solutions per sample,
                59 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

Related Work

Please check out the most related work which presents a five-points based approach for the computation of the fundamental matrix based on planes in the scene.

Barath,D Five-point fundamental matrix estimation for uncalibrated cameras .In CVPR, 2018. Paper


We thank the anonymous reviewers for improving our paper.
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