Poster Abstract: mmWaveNet: Indoor Point Cloud Generation from Millimeter-Wave Devices

Millimeter wave (mmWave) 3D imaging has been applied for point cloud data (PCD) generation due to its valuable attributes, such as working under low light, compact size, and low-cost. However, past works have focused on transforming millimeter wave reflection signals into other data structures, like polar images and coarse PCDs before applying neural network to produce dense PCDs. Those algorithms will filter some useful features. To address this issue, our paper proposes an innovative prototype: mmWaveNet, a deep learning model that directly uses reflection signals as input and generates high-quality PCDs. We have experimentally evaluated mmWaveNet in a large indoor environment.