RadarGen：基于摄像头的汽车雷达点云生成技术

摘要

我们提出RadarGen——一种基于多视角摄像图像生成逼真汽车雷达点云的扩散模型。该模型通过将雷达测量值表示为鸟瞰图形式，实现对空间结构、雷达散射截面（RCS）及多普勒属性的联合编码，从而将高效的图像潜在扩散技术适配到雷达领域。轻量级的重建步骤可从生成的特征图中恢复点云。为增强生成结果与视觉场景的一致性，RadarGen融合了从预训练基础模型中提取的BEV对齐深度、语义和运动线索，这些线索引导随机生成过程产生物理合理的雷达模式。基于图像的条件生成机制使该方法原则上能广泛兼容现有视觉数据集与仿真框架，为多模态生成式仿真提供了可扩展路径。大规模驾驶数据评估表明，RadarGen能准确捕捉雷达测量的特征分布，并缩小与真实数据训练的感知模型之间的性能差距，标志着跨传感模态统一生成仿真迈出了重要一步。

English

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.

RadarGen：基于摄像头的汽车雷达点云生成技术

RadarGen: Automotive Radar Point Cloud Generation from Cameras

摘要

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