Separable Four Points Fundamental Matrix

TL;DR

This paper introduces a new RANSAC-based method for computing the fundamental matrix using epipolar homography decomposition, enabling faster and more accurate results with minimal hypotheses.

Contribution

It presents a novel decomposition-based approach that guarantees minimal hypotheses evaluation under certain conditions, improving efficiency over existing methods.

Findings

Fast and accurate fundamental matrix estimation on real-world data

Guarantees minimal hypotheses evaluation when four correspondences lie on an image line

Provides a geometrically meaningful decomposition-based sampling strategy

Abstract

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.