RadarTwin：面向移动室内感知的场景特定毫米波雷达仿真与学习
RadarTwin: Scene-Specific mmWave Radar Simulation and Learning for Mobile Indoor Perception
AI总结：
提出RadarTwin框架，利用3D重建和视觉语言模型推断材料属性，通过物理射线追踪合成FMCW雷达数据，解决雷达数据稀缺问题，仿真训练模型在真实场景中识别物体准确率达95.3%。
摘要：
毫米波雷达感知受限于数据稀缺：基于现有雷达数据集训练的模型无法泛化到新物体、环境和感知轨迹。我们提出RadarTwin，一个在真实数据收集之前生成部署特定雷达训练数据的框架。给定目标空间的3D重建（手机LiDAR、机器人搭载的传感或RGB转3D），RadarTwin使用视觉语言模型推断雷达相关的表面材料，并利用基于物理的射线追踪器合成原始调频连续波（FMCW）雷达测量，包含多径传播。为了研究从仿真到现实的迁移，我们收集了一个配对的真实-仿真数据集，涵盖家居物体、材料类别、距离、旋转、平移和移动感知轨迹。我们表明，仿真和真实雷达共享相同的物体判别形状和材料特征，并且对环境的建模多径对于匹配真实测量至关重要。仅基于仿真训练的表征在无任何真实雷达标签的情况下识别真实物体的准确率是随机水平的2.5倍，而少量标注样本可将12类识别任务的准确率提升至95.3%。RadarTwin使得在收集任何真实雷达数据之前，即可为新空间训练雷达感知模型。
英文摘要：
Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, environments, and sensing trajectories. We present RadarTwin, a framework for generating deployment-specific radar training data before real data collection. Given a 3D reconstruction of a target space (phone LiDAR, robot-mounted sensing, or RGB-to-3D), RadarTwin uses a vision-language model to infer radar-relevant surface materials and a physics-based ray tracer to synthesize raw frequency-modulated continuous-wave (FMCW) radar measurements with multi-bounce propagation. To study what transfers from simulation to reality, we collect a paired real-simulated dataset spanning household objects, material classes, distances, rotations, translations, and mobile sensing trajectories. We show that simulated and real radar share the same object-discriminative shape and material features, and that modeling the environment's multipath is essential to matching real measurements. A representation trained on simulation alone recognizes real objects at 2.5 times chance with no real radar labels, and a few labeled examples raise this to 95.3% on a 12-way recognition task. RadarTwin enables training radar perception for a new space before any real radar data is collected there.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
