  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.
