单帧毫米波雷达生成精细点云，无需运动或合成孔径。
RadarSFD: Single-Frame Diffusion with Pretrained Priors for Radar Point Clouds
- 用预训练深度模型的几何先验引导扩散过程
- 在RadarHD上优于基线模型，恢复细墙与窄缝隙
- 适合小型无人机、可穿戴设备等紧凑系统
毫米波雷达在雾、烟、尘及低光条件下具备鲁棒感知能力，适用于尺寸、重量和功耗受限的机器人平台。现有雷达成像方法通常依赖合成孔径或多帧融合以提升分辨率，这对小型飞行器、巡检或可穿戴系统不切实际。本文提出RadarSFD，一种条件潜变量扩散框架，仅用单帧雷达数据即可重建出类似激光雷达的密集点云，无需运动信息或合成孔径。该方法将预训练单目深度估计器的几何先验迁移至扩散主干网络，通过通道级潜变量拼接将先验锚定于雷达输入，并采用结合潜空间与像素空间损失的双空间目标函数进行输出正则化。在RadarHD基准测试中，RadarSFD性能达到当前最优水平。定性结果显示能恢复细墙与窄缝隙，跨新环境实验验证了强泛化能力。消融实验表明预训练初始化、雷达BEV条件输入及双空间损失均至关重要。这些结果共同建立了一条面向紧凑机器人系统的实用单帧、无SAR毫米波雷达密集点云感知管道。
原文摘要 · Abstract (English)
Millimeter-wave radar provides robust perception in fog, smoke, dust, and low light, making it attractive for size-, weight-, and power-constrained robotic platforms. Existing radar imaging methods typically rely on synthetic aperture or multi-frame aggregation to improve resolution, which is impractical for small aerial, inspection, or wearable systems. We present RadarSFD, a conditional latent diffusion framework that reconstructs dense LiDAR-like point clouds from a single radar frame without motion or SAR. Our approach transfers geometric priors from a pretrained monocular depth estimator into the diffusion backbone, anchors them to radar inputs via channel-wise latent concatenation, and regularizes outputs with a dual-space objective combining latent and pixel-space losses. On the RadarHD benchmark, RadarSFD achieves state-of-the-art performance against baseline models. Qualitative results show recovery of fine walls and narrow gaps, and experiments across new environments confirm strong generalization. Ablation studies highlight the importance of pretrained initialization, radar BEV conditioning, and the dual-space loss. Together, these results establish a practical single-frame, no-SAR mmWave radar pipeline for dense point cloud perception in compact robotic systems.
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