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.

翻译：神经场作为场景表示方法已被广泛研究，用于复现和生成包括自动驾驶车辆及机器人需处理的多样化室外场景。尽管基于RGB和LiDAR数据的方法已取得显著成功，但针对雷达这一传感模态的神经重建方法仍鲜有探索。工作于毫米波段的雷达传感器对雾雨中的散射具有鲁棒性，因此为主动与被动光学传感技术提供了互补模态。此外，现有雷达传感器成本效益极高，并已广泛部署于户外运行的机器人与车辆中。本文提出雷达场——一种专为有源雷达成像器设计的神经场景重建方法。该方法将显式的物理感知传感器模型与隐式神经几何及反射模型相结合，以直接合成原始雷达测量值并提取场景占有信息。所提方法不依赖体渲染，而是通过傅里叶频域中的场学习，利用原始雷达数据监督。我们在多种室外场景中验证了该方法的有效性，包括密集车辆与基础设施的城市场景，以及毫米波传感具有特别优势的恶劣天气场景。