Leveraging Scale- and Orientation-Covariant Features for Planar Motion Estimation
Abstract
In this paper, we derive a linear constraint for planar motion leveraging scale- and orientation-covariant features, , SIFT, which is used to create a novel minimal solver for planar motion requiring only a single covariant feature. We compare the proposed method to traditional point-based solvers and solvers relying on affine correspondences in controlled synthetic environments and well-established datasets for autonomous driving. The proposed solver is integrated into a modern robust estimation framework, where it is shown to accelerate the complete estimation pipeline more than 25×, compared to state-of-the-art affine-based minimal solvers, with negligible loss in precision1 . 1 Code available here: https://github.com/EricssonResearch/eccv-2024
Cite
Text
Örnhag and Jaenal. "Leveraging Scale- and Orientation-Covariant Features for Planar Motion Estimation." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-72949-2_24Markdown
[Örnhag and Jaenal. "Leveraging Scale- and Orientation-Covariant Features for Planar Motion Estimation." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/ornhag2024eccv-leveraging/) doi:10.1007/978-3-031-72949-2_24BibTeX
@inproceedings{ornhag2024eccv-leveraging,
title = {{Leveraging Scale- and Orientation-Covariant Features for Planar Motion Estimation}},
author = {Örnhag, Marcus Valtonen and Jaenal, Alberto},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-72949-2_24},
url = {https://mlanthology.org/eccv/2024/ornhag2024eccv-leveraging/}
}