Radar perception models are trained with different inputs, from range-Doppler spectra to sparse point clouds. Dense spectra are assumed to outperform sparse point clouds, yet they can vary considerably across sensors and configurations, which hinders transfer. In this paper, we provide alternatives for incorporating spectral information into radar point clouds and show that, point clouds need not underperform compared to spectra. We introduce the spectral point cloud paradigm, where point clouds are treated as sparse, compressed representations of the radar spectra, and argue that, when enriched with spectral information, they serve as strong candidates for a unified input representation that is more robust against sensor-specific differences. We develop an experimental framework that compares spectral point cloud (PC) models at varying densities against a dense range-Doppler (RD) benchmark, and report the density levels where the PC configurations meet the performance of the RD benchmark. Furthermore, we experiment with two basic spectral enrichment approaches, that inject additional target-relevant information into the point clouds. Contrary to the common belief that the dense RD approach is superior, we show that point clouds can do just as well, and can surpass the RD benchmark when enrichment is applied. Spectral point clouds can therefore serve as strong candidates for unified radar perception, paving the way for future radar foundation models.

翻译：雷达感知模型基于不同输入进行训练，从距离-多普勒光谱到稀疏点云。尽管密集光谱被认为优于稀疏点云，但其在不同传感器及配置间差异显著，这阻碍了模型的迁移能力。本文提出了将光谱信息融入雷达点云的替代方案，并证明点云的性能未必逊色于光谱。我们引入光谱点云范式，将点云视为雷达光谱的稀疏压缩表示，并论证经过光谱信息增强后，点云可作为对传感器差异更鲁棒的统一输入表示的强有力候选。我们构建了一个实验框架，在不同密度条件下将光谱点云模型与密集距离-多普勒基准进行对比，并报告了点云配置达到基准性能所需的密度水平。此外，我们实验了两种基础光谱增强方法，将额外目标相关信息注入点云。与普遍认为密集距离-多普勒方法更优的观点相反，我们证明点云性能同样出色，且在应用增强后可超越该基准。因此，光谱点云可作为统一雷达感知的强大候选方案，为未来雷达基础模型奠定基础。