用摄像头高分辨率生成更清晰的雷达图像，提升仿真效果。
RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields
- 基于相机初始化的神经场，融合视觉与雷达数据
- 生成的多普勒雷达距离图更清晰，几何细节更丰富
- 适合雷达感知系统开发与算法验证
雷达是相机的理想补充：两者均为低成本、固态传感器，相机提供精细角度分辨率，而雷达具备测距能力且在恶劣天气下表现稳定。然而，雷达数据比图像更难解析，且不同传感器间差异显著，因此需依赖仿真来设计传感器与处理流程。近期研究将雷达重建视为新视角合成问题，在还原雷达相关几何和模拟低层雷达数据方面展现出巨大潜力。但此类方法受限于雷达本身较低的空间分辨率。为此，我们提出统一可微渲染器RadarSim，利用相机的高角分辨率，从相机初始化的神经场生成多普勒雷达距离图。基于自研手持设备采集的校准雷达-相机数据集，实验表明RadarSim生成的几何结构和多普勒距离帧均优于仅使用雷达的重建结果。
原文摘要 · Abstract (English)
Radars are an ideal complement to cameras: both are inexpensive, solid-state sensors, with cameras offering fine angular resolution, while radars provide metric depth and robustness under adverse weather. However, radar data is more difficult to interpret than camera images and varies significantly between sensors, necessitating increased reliance on simulation for prototyping sensors and processing pipelines. Recent work treating radar reconstruction as a novel view synthesis problem has shown great promise in reconstructing radar-relevant geometry and simulating low-level radar data. However, such methods are constrained by the low spatial resolution of the underlying radar. To address this, we propose a unified differentiable renderer, RadarSim, which leverages the high angular resolution of RGB cameras to generate Doppler radar range images from a camera-initialized neural field. Using a novel data set of calibrated radar camera recordings from a custom hand-held rig, we demonstrate that RadarSim produces sharper geometry and Doppler range frames than radar-only reconstructions.
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