用无标签毫米波数据和激光雷达数据扩充人体姿态数据集，提升模型泛化能力。
Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets
- 用伪标签和闭式转换生成新毫米波数据
- 在域内和域外设置下分别降低15.1%和18.9%误差
- 适合需要多样化训练数据的毫米波姿态估计研究者
当前用于人体姿态估计（HPE）的毫米波（mmWave）数据集稀缺，且点云属性与人体姿态多样性不足，限制了模型泛化能力。另一方面，大量无标签的mmWave HPE数据和多样化的LiDAR HPE数据集可获取。我们提出EMDUL，一种利用无标签mmWave数据和LiDAR数据集扩展现有mmWave数据集的新方法。EMDUL包含两个独立模块：伪标签估计算法为无标签mmWave数据打标签，闭式转换器将标注的LiDAR点云转换为对应的mmWave点云。通过融合经转换的LiDAR数据和伪标签的mmWave数据，显著提升了所有测试模型的性能与泛化能力，在域内和域外设置下分别降低15.1%和18.9%的误差。代码已开源于https://github.com/Shimmer93/EMDUL。
原文摘要 · Abstract (English)
Current millimeter-wave (mmWave) datasets for human pose estimation (HPE) are scarce and lack diversity in both point cloud (PC) attributes and human poses, hindering the generalization ability of their trained models. On the other hand, unlabeled mmWave HPE data and diverse LiDAR HPE datasets are readily available. We propose EMDUL, a novel approach to expand the volume and diversity of an existing mmWave dataset using unlabeled mmWave data and LiDAR datasets. EMDUL consists of two independent modules, namely a pseudo-label estimator to annotate unlabeled mmWave data, and a closed-form converter that translates an annotated LiDAR PC to its mmWave counterpart. Expanding the original dataset with both LiDAR-converted and pseudo-labeled mmWave PCs significantly boosts the performance and generalization ability of all the examined HPE models, reducing 15.1% and 18.9% error for in-domain and out-of-domain settings, respectively. Code is available at https://github.com/Shimmer93/EMDUL.
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