Affine Correspondences Between Central Cameras for Rapid Relative Pose Estimation
Abstract
This paper presents a novel algorithm to estimate the relative pose, i.e. the 3D rotation and translation of two cameras, from two affine correspondences (ACs) considering any central camera model. The solver is built on new epipolar constraints describing the relationship of an AC and any central views. We also show that the pinhole case is a specialization of the proposed approach. Benefiting from the low number of required correspondences, robust estimators like LO-RANSAC need fewer samples, and thus terminate earlier than using the five-point method. Tests on publicly available datasets containing pinhole, fisheye and catadioptric camera images confirmed that the method often leads to results superior to the state-of-the-art in terms of geometric accuracy.
Cite
Text
Eichhardt and Chetverikov. "Affine Correspondences Between Central Cameras for Rapid Relative Pose Estimation." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01231-1_30Markdown
[Eichhardt and Chetverikov. "Affine Correspondences Between Central Cameras for Rapid Relative Pose Estimation." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/eichhardt2018eccv-affine/) doi:10.1007/978-3-030-01231-1_30BibTeX
@inproceedings{eichhardt2018eccv-affine,
title = {{Affine Correspondences Between Central Cameras for Rapid Relative Pose Estimation}},
author = {Eichhardt, Ivan and Chetverikov, Dmitry},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01231-1_30},
url = {https://mlanthology.org/eccv/2018/eichhardt2018eccv-affine/}
}