DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes

• 类型：arxiv
• 标识：2609.39841
• 链接：https://arxiv.org/abs/2609.39841
• 主分类：evaluation
• 形态：benchmark
• TLDR：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 i
• 待LLM分类：否
• 标题中文：DyRAD：面向动态驾驶场景的雷达新视角合成
• TLDR中文：从记录的传感器数据重建动态驾驶场景，可通过合成原始轨迹之外的观测来支持自动驾驶系统的闭环评估。与相机和 LiDAR 不同，雷达通过多普勒效应直接测量径向速度。然而现有雷达新视角合成方法未能利用这一能力：处理动态场景的方法仅重建 range-azimuth 张量，而能渲染多普勒信息的方法又仅适用于静态场景。此外，由于雷达处理会将每个反射扩散到多个 bin 中，现有表征将这种扩散直接吸收进
• 来源文件：
• /inbox/tom/_candidates/2026-10-01-agent-rag-longcontext-candidates.json