利用无标注数据与激光雷达数据集扩充用于人体姿态估计的毫米波数据集
Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets
对做毫米波人体姿态估计的研究者，这篇论文提供了一条可复用的数据扩充路径：用闭式转换把带标注的激光雷达点云映射成毫米波点云，再用伪标签估计器标注无标注毫米波数据，从而绕开毫米波标注昂贵、数据稀缺的瓶颈，直接提升模型的域内与跨域泛化。
原文依据 a closed-form converter that translates an annotated LiDAR PC to its mmWave counterpart
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论当前用于人体姿态估计（HPE）的毫米波（mmWave）数据集十分稀缺，且在点云属性与人体姿态两方面都缺乏多样性，这制约了在其上训练模型的泛化能力。另一方面，无标注的毫米波人体姿态估计数据与多样化的激光雷达人体姿态估计数据集则易于获取。我们提出 EMDUL，一种利用无标注毫米波数据和激光雷达数据集来扩充现有毫米波数据集规模与多样性的新方法。EMDUL 由两个相互独立的模块构成，即用于标注无标注毫米波数据的伪标签估计器，以及将带标注的激光雷达点云转换为其毫米波对应形式的闭式转换器。用经激光雷达转换得到的毫米波点云与伪标签毫米波点云共同扩充原始数据集，显著提升了所有受测人体姿态估计模型的性能与泛化能力，在域内与域外设定下分别将误差降低了 15.1% 与 18.9%。代码见 https://github.com/Shimmer93/EMDUL。
英文原文摘要 995 字符
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.