  Neural fields have been broadly investigated as scene representations for the
reproduction and novel generation of diverse outdoor scenes, including those
autonomous vehicles and robots must handle. While successful approaches for RGB
and LiDAR data exist, neural reconstruction methods for radar as a sensing
modality have been largely unexplored. Operating at millimeter wavelengths,
radar sensors are robust to scattering in fog and rain, and, as such, offer a
complementary modality to active and passive optical sensing techniques.
Moreover, existing radar sensors are highly cost-effective and deployed broadly
in robots and vehicles that operate outdoors. We introduce Radar Fields - a
neural scene reconstruction method designed for active radar imagers. Our
approach unites an explicit, physics-informed sensor model with an implicit
neural geometry and reflectance model to directly synthesize raw radar
measurements and extract scene occupancy. The proposed method does not rely on
volume rendering. Instead, we learn fields in Fourier frequency space,
supervised with raw radar data. We validate the effectiveness of the method
across diverse outdoor scenarios, including urban scenes with dense vehicles
and infrastructure, and in harsh weather scenarios, where mm-wavelength sensing
is especially favorable.
