  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.
