构建首个双雷达毫米波人体网格重建数据集与评估基准。
DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction
- 提出多雷达融合框架mmPTM，联合点云与成像管特征进行重建。
- 在36万帧数据上验证，跨场景下精度优于现有方法。
- 适合研究毫米波人体感知、多模态融合的学者使用。
毫米波雷达在无接触、隐私保护和鲁棒人体感知方面展现巨大潜力，但现有基于毫米波的人体网格重建（HMR）研究受限于缺乏用于泛化分析的基准及算法公平比较平台。为此，我们提出DGHMesh，一个大规模双雷达毫米波数据集与聚焦泛化的评估基准。该数据集包含15名受试者执行8种动作的36万帧同步数据，涵盖调频连续波（FMCW）雷达、步进频率连续波（SFCW）雷达、RGB图像及高精度3D HMR标注。同时提供两模态雷达的原始I/Q数据与精确校准的空间位置信息。基准支持多种测量配置下的评估，包括人体位置偏移、朝向变化、子阵列尺寸调整以及跨被试设置。基于DGHMesh，我们还提出mmPTM——一种基于查询的多雷达融合框架，联合利用点云与成像管特征实现人体网格重建。在不同设置下与代表性基线对比的大量实验表明，mmPTM在多个子基准中均表现出优异的准确性和竞争力的泛化能力，验证了多雷达融合的有效性及所提数据集与基准的实际价值。DGHMesh与mmPTM已在GitHub公开（https://github.com/SPIresearch/DGHMesh），完整基准与代码将在论文发表后释放。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) radar has shown great potential for contactless, privacy-preserving, and robust human sensing, yet existing mmWave-based human mesh reconstruction (HMR) studies are still limited by the lack of benchmarks for generalization analysis under configuration shifts and fair comparison of different algorithms. To address the limitation, we present DGHMesh, a large-scale dual-radar mmWave dataset and generalization-focused benchmark for HMR. It contains data from 15 subjects performing 8 actions, with 360,000 synchronized frames collected from FMCW radar, SFCW radar, RGB images, and high-precision 3D HMR annotations. In addition, the dataset provides synchronized raw I/Q data from both radar modalities and accurately calibrated radar spatial positions. The benchmark is designed to evaluate HMR methods under diverse measurement configurations, including human position shifts, human orientation shifts, subarray size variations, and cross-subject settings. Based on DGHMesh, we also propose mmPTM, a query-based multi-radar fusion framework that jointly exploits point clouds and imaging tubes for HMR. Extensive experiments are conducted against representative baselines under different settings. The results demonstrate that mmPTM consistently achieves outstanding accuracy and competitive generalization capability across multiple sub-benchmarks, validating the effectiveness of multi-radar fusion and the practical value of the proposed dataset and benchmark for mmWave-based HMR research. DGHMesh and mmPTM are publicly available at https://github.com/SPIresearch/DGHMesh.(The complete benchmark and code will be released after paper publication)
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