雷达也能拍“动态街景”了，还能换配置直接重渲染
自动驾驶测试有个痛点：车只能在跑过的路上反复测，没跑过的地方没法验证。这篇让雷达也能像相机一样“重放”没走过的视角——而且它不只是重建画面，连目标的径向速度(多普勒)都一起重建出来，这是相机和激光雷达做不到的。做法是把场景拆成静态背景和动态目标两类反射体，速度从目标轨迹推算、再投影到视线方向，多普勒既是渲染结果、又反过来监督轨迹。关键一招是：雷达信号处理会把一个反射点摊到多个格子，以前的方法把这摊开的效果当成场景本身，视角一动就穿帮；这篇用固定的解析点扩散函数来渲染，把传感器造成的模糊和场景几何彻底分开。好处不止更准：同一场景还能零成本换雷达配置(不同频段、不同分辨率)直接重渲染，不用重新拟合。在 RADIal 数据集上，它能在 90.7% 的参考目标里找回雷达检测，最强基线只有 26.9%。这不是你明天能用的东西，但它让“用仿真数据测自动驾驶”离真实雷达又近了一步。
📄 原文摘要(英文)
Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observations beyond the original trajectory. Unlike cameras and LiDAR, radar measures radial velocity directly through Doppler. Yet existing radar novel-view synthesis fails to exploit this capability: methods addressing dynamic scenes reconstruct only range-azimuth tensors, while methods that render Doppler assume static scenes. Moreover, because radar processing spreads each reflection across multiple bins, existing representations absorb this spread into scene geometry, causing it to render incorrectly when the viewpoint moves. We present DyRAD, which models dynamic driving scenes using static background reflectors and motion-tracked dynamic point reflectors to render complete range-azimuth-Doppler (RAD) tensors. Reflector velocities are derived from object tracks and projected onto the line of sight, making Doppler both a rendered output and supervision for those tracks. Crucially, we render reflectors through a fixed analytic point-spread function (PSF) derived from the radar's signal-processing chain, preventing sensor-induced spread from being baked into the scene representation. Beyond improving scene reconstruction, this separation also enables zero-shot sensor-configuration transfer, allowing the same reconstructed scene to be rendered under different radar specifications without refitting. We evaluate DyRAD on RADIal, Boreas, and a synthetic benchmark across both on-path poses and displaced viewpoints untested by prior work. On RADIal, DyRAD recovers radar detections in 90.7% of reference-detected objects, compared with 26.9% for the strongest baseline.