Separable Four Points Fundamental Matrix
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.
Cite
Text
Ben-Artzi. "Separable Four Points Fundamental Matrix." Winter Conference on Applications of Computer Vision, 2021.Markdown
[Ben-Artzi. "Separable Four Points Fundamental Matrix." Winter Conference on Applications of Computer Vision, 2021.](https://mlanthology.org/wacv/2021/benartzi2021wacv-separable/)BibTeX
@inproceedings{benartzi2021wacv-separable,
title = {{Separable Four Points Fundamental Matrix}},
author = {Ben-Artzi, Gil},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2021},
pages = {188-196},
url = {https://mlanthology.org/wacv/2021/benartzi2021wacv-separable/}
}