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.

翻译：辐射场在多视角图像集合的逆渲染、新视角合成和复杂场景三维建模领域取得了重大突破。自其提出以来，人们已证明辐射场可扩展至其他模态，如激光雷达、射频、X射线或超声。本文表明，尽管光学与合成孔径雷达（SAR）图像形成模型存在显著差异，但将辐射场扩展至雷达图像是可行的，从而首次提出"雷达场"。这使得我们能够仅利用雷达图像集合学习表面模型，其学习方式与常规辐射场相同，且计算复杂度平均相当。得益于两种场定义方式的相似性，本研究还展示了结合光学图像与SAR图像的混合方法潜力。