MiliPoint: A Point Cloud Dataset for mmWave Radar
This work provides a foundational dataset for the deep learning community to explore mmWave radar applications in human activity recognition, though it is incremental as it focuses on data collection and baseline establishment rather than novel method development.
The authors tackled the challenge of developing effective point set-based deep learning methods for mmWave radar data by creating MiliPoint, a large-scale, open dataset for human activity recognition, which is larger and more diverse than existing datasets and includes baseline results from established point-based neural networks.
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.