Radar Fields: An Extension of Radiance Fields to SAR
Abstract
Radiance fields have been a major breakthrough in the field of inverse rendering, novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Since their introduction, it was shown that they could be extended to other modalities such as LiDAR, radio frequencies, X-ray or ultrasound. In this paper, we show that, despite the important difference between optical and synthetic aperture radar (SAR) image formation models, it is possible to extend radiance fields to radar images thus presenting the first "radar fields". This allows us to learn surface models using only collections of radar images, similar to how regular radiance fields are learned and with the same computational complexity on average. Thanks to similarities in how both fields are defined, this work also shows a potential for hybrid methods combining both optical and SAR images.
Cite
Text
Ehret et al. "Radar Fields: An Extension of Radiance Fields to SAR." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00061Markdown
[Ehret et al. "Radar Fields: An Extension of Radiance Fields to SAR." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/ehret2024cvprw-radar/) doi:10.1109/CVPRW63382.2024.00061BibTeX
@inproceedings{ehret2024cvprw-radar,
title = {{Radar Fields: An Extension of Radiance Fields to SAR}},
author = {Ehret, Thibaud and Marí, Roger and Derksen, Dawa and Gasnier, Nicolas and Facciolo, Gabriele},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {564-574},
doi = {10.1109/CVPRW63382.2024.00061},
url = {https://mlanthology.org/cvprw/2024/ehret2024cvprw-radar/}
}