Graph neural networks (GNNs) have gained significant attention for their effectiveness across various domains. This study focuses on applying GNN to process 3D point cloud data for human pose estimation (HPE) and human activity recognition (HAR). We propose novel point cloud feature extraction (PCFEx) techniques to capture meaningful information at the point, edge, and graph levels of the point cloud by considering point cloud as a graph. Moreover, we introduce a GNN architecture designed to efficiently process these features. Our approach is evaluated on four most popular publicly available millimeter wave radar datasets, three for HPE and one for HAR. The results show substantial improvements, with significantly reduced errors in all three HPE benchmarks, and an overall accuracy of 98.8% in mmWave-based HAR, outperforming the existing state of the art models. This work demonstrates the great potential of feature extraction incorporated with GNN modeling approach to enhance the precision of point cloud processing.

翻译：图神经网络（GNN）因其在各个领域的卓越表现而受到广泛关注。本研究聚焦于将GNN应用于处理三维点云数据，以进行人体姿态估计（HPE）与人体活动识别（HAR）。我们提出了新颖的点云特征提取（PCFEx）技术，通过将点云视为图结构，在点、边和图三个层次上捕获有意义的信息。此外，我们设计了一种能够高效处理这些特征的GNN架构。我们在四个最常用的公开毫米波雷达数据集上对所提方法进行了评估，其中三个用于HPE，一个用于HAR。实验结果表明，该方法在所有三个HPE基准测试中均显著降低了误差，并在基于毫米波的HAR任务中取得了98.8%的整体准确率，性能超越了现有最优模型。本工作证明了特征提取与GNN建模方法相结合，在提升点云处理精度方面具有巨大潜力。