用普通毫米波雷达实现车辆场景下的高精度3D物体重建
3D Object Reconstruction with mmWave Radars
- 双雷达融合正交视角数据，结合运动补偿提升点云密度
- 无需物体先验框，通过定制编码器-解码器模型重建3D形状
- 在汽车、自行车和行人上表现良好，媲美深度相机+激光雷达结果
本文提出RFconstruct框架，利用商用毫米波雷达在自动驾驶场景中实现3D物体形状重建。针对雷达固有的低角分辨率、镜面反射及点云稀疏问题，通过软硬件协同设计，整合两个成像正交平面的雷达设备数据，并进行运动感知的时间融合，生成更稠密的3D点云。随后，采用无需物体边界框先验的定制编码器-解码器模型进行3D形状重建。实验将RFconstruct的重建效果与配备激光雷达的深度相机提取的3D模型对比，结果表明其能准确重建汽车、自行车和行人的3D结构。
原文摘要 · Abstract (English)
This paper presents RFconstruct, a framework that enables 3D shape reconstruction using commercial off-the-shelf (COTS) mmWave radars for self-driving scenarios. RFconstruct overcomes radar limitations of low angular resolution, specularity, and sparsity in radar point clouds through a holistic system design that addresses hardware, data processing, and machine learning challenges. The first step is fusing data captured by two radar devices that image orthogonal planes, then performing odometry-aware temporal fusion to generate denser 3D point clouds. RFconstruct then reconstructs 3D shapes of objects using a customized encoder-decoder model that does not require prior knowledge of the object's bound box. The shape reconstruction performance of RFconstruct is compared against 3D models extracted from a depth camera equipped with a LiDAR. We show that RFconstruct can accurately generate 3D shapes of cars, bikes, and pedestrians.
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