RadarGen: Automotive Radar Point Cloud Generation from Cameras

TL;DR

RadarGen is a diffusion-based model that synthesizes realistic automotive radar point clouds from multi-view camera images, integrating spatial, RCS, and Doppler data for improved simulation and perception.

Contribution

The paper introduces RadarGen, a novel diffusion model that generates radar point clouds from camera images, incorporating BEV-aligned cues for physically plausible radar simulation.

Findings

RadarGen accurately captures radar measurement distributions.

It reduces the gap between simulated and real perception data.

The method is compatible with existing visual datasets.

Abstract

We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form that encodes spatial structure together with radar cross section (RCS) and Doppler attributes. A lightweight recovery step reconstructs point clouds from the generated maps. To better align generation with the visual scene, RadarGen incorporates BEV-aligned depth, semantic, and motion cues extracted from pretrained foundation models, which guide the stochastic generation process toward physically plausible radar patterns. Conditioning on images makes the approach broadly compatible, in principle, with existing visual datasets and simulation frameworks, offering a scalable direction for multimodal generative simulation. Evaluations on large-scale driving data show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data, marking a step toward unified generative simulation across sensing modalities.