提出点云图神经网络特征提取方法，提升人体姿态与活动识别精度
PCFEx: Point Cloud Feature Extraction for Graph Neural Networks
- 将点云建模为图，从点、边、图三层次提取特征
- 在三个姿态估计数据集上误差显著降低，毫米波活动识别准确率达98.8%
- 适合需要高精度点云处理的智能感知场景
图神经网络（GNN）在多个领域表现出色。本文聚焦于将GNN应用于3D点云数据，用于人体姿态估计（HPE）和人体活动识别（HAR）。我们提出一种新型点云特征提取（PCFEx）技术，通过将点云视为图，在点、边和图三个层次捕捉有意义的信息。同时，设计了一种高效的GNN架构以处理这些特征。该方法在四个主流毫米波雷达数据集上进行评估，其中三个用于HPE，一个用于HAR。结果表明，在所有三个HPE基准测试中误差均显著降低，毫米波基活动识别整体准确率达到98.8%，优于现有最先进模型。本工作展示了融合特征提取与图建模在提升点云处理精度方面的巨大潜力。
原文摘要 · Abstract (English)
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.
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