RadarGen: Automotive Radar Point Cloud Generation from Cameras

We present RadarGen, a diffusion model for synthesizing re-alistic automotive radar point clouds from multi-view camera imagery.RadarGen adapts efficient image-latent diffusion to the radar domain byrepresenting radar measurements in bird’s-eye-view form that encodesspatial structure together with radar cross section (RCS) and Dopplerattributes. A lightweight recovery step reconstructs point clouds fromthe generated maps. To better align generation with the visual scene,RadarGen incorporates BEV-aligned depth, semantic, and motion cuesextracted from pretrained foundation models, which guide the stochasticgeneration process toward physically plausible radar patterns. Condition-ing on images makes the approach broadly compatible, in principle, withexisting visual datasets and simulation frameworks, offering a scalable di-rection for multimodal generative simulation. Evaluations on large-scaledriving data show that RadarGen captures characteristic radar measure-ment distributions and reduces the gap to perception models trained onreal data, marking a step toward unified generative simulation acrosssensing modalities.