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RadarGen, a diffusion model, generates realistic automotive radar point clouds from multi-view camera imagery by adapting image-latent diffusion to the radar domain. It represents radar measurements in bird's-eye-view form, encoding spatial structure, RCS, and Doppler attributes, and uses a lightweight recovery step to reconstruct point clouds. By conditioning on BEV-aligned depth, semantic, and motion cues extracted from pretrained foundation models, RadarGen aligns generation with the visual scene, leading to physically plausible radar patterns and improved performance on perception tasks.
Synthesizing realistic radar data from camera images is now possible, bridging the gap between visual and radar perception for autonomous driving.
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.
NVIDIA