Millimeter Wave (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWave Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors.

翻译：毫米波雷达因能在烟雾、雨雪、弱光等恶劣环境中工作而日益受到关注。已有研究探索了从噪声大且稀疏的毫米波雷达信号中重建三维骨架或网格的可能性。然而，在跨场景条件下，毫米波信号对人体三维重建的精度及其与相机相比的表现尚不明确——这些问题对于单独使用毫米波雷达或将其与相机联合使用至关重要。为回答这些问题，本文首先设计并构建了一套自动三维人体标注系统，该系统配备多传感器以采集大规模数据集。该数据集包含不同场景下经同步校准的毫米波雷达点云与RGB(D)图像，以及场景中人体的骨架/网格标注。基于该数据集，我们使用来自不同传感器的输入训练了多种最先进方法，并在各类场景中进行测试。结果表明：1）尽管生成的雷达点云存在噪声与稀疏性，毫米波雷达的重建精度仍优于RGB相机，但逊于深度相机；2）毫米波雷达的重建受恶劣天气影响适中，而RGB(D)相机则受严重影响。此外，对数据集与结果的深入分析为改进毫米波雷达重建及多传感器信号融合提供了启示。