PCFEx: Point Cloud Feature Extraction for Graph Neural Networks

TL;DR

This paper introduces PCFEx, a novel point cloud feature extraction method integrated with GNNs, significantly improving human pose estimation and activity recognition accuracy using radar data.

Contribution

The paper proposes a new feature extraction technique for point clouds and a GNN architecture tailored for processing these features in 3D human pose and activity tasks.

Findings

Substantial error reduction in HPE benchmarks

Achieved 98.8% accuracy in mmWave HAR

Outperformed existing state-of-the-art models

Abstract

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.