Millimetre-wave (mmWave) radar has emerged as an attractive and cost-effective alternative for human activity sensing compared to traditional camera-based systems. mmWave radars are also non-intrusive, providing better protection for user privacy. However, as a Radio Frequency (RF) based technology, mmWave radars rely on capturing reflected signals from objects, making them more prone to noise compared to cameras. This raises an intriguing question for the deep learning community: Can we develop more effective point set-based deep learning methods for such attractive sensors? To answer this question, our work, termed MiliPoint, delves into this idea by providing a large-scale, open dataset for the community to explore how mmWave radars can be utilised for human activity recognition. Moreover, MiliPoint stands out as it is larger in size than existing datasets, has more diverse human actions represented, and encompasses all three key tasks in human activity recognition. We have also established a range of point-based deep neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint, which can serve to set the ground baseline for further development.

翻译：毫米波雷达作为一种有吸引力且成本效益高的替代方案，相较于传统基于摄像头的系统，已广泛应用于人体活动感知领域。毫米波雷达还具有非侵入性特点，能更好地保护用户隐私。然而，作为射频技术，毫米波雷达依赖捕捉物体反射信号，使其相较于摄像头更容易受到噪声干扰。这为深度学习领域提出了一个引人深思的问题：能否针对这类具有吸引力的传感器，开发更高效的点集深度学习方法？为回答此问题，本研究提出了MiliPoint——一个大规模开放数据集，旨在探索如何利用毫米波雷达进行人体活动识别。与现有数据集相比，MiliPoint的显著优势在于：数据规模更大、涵盖的人体动作类别更丰富，并完整包含人体活动识别中的三项关键任务。我们还在MiliPoint上建立了包括DGCNN、PointNet++和PointTransformer在内的多种基于点的深度神经网络基准模型，为后续研究奠定基础。