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

被引：0

作者：

Gu, Zhuangzhuang ^{[1
]}

Sur, Sanjib ^{[1
]}

机构：

[1] Univ South Carolina, Dept Comp Sci & Engn, Columbia, SC 29208 USA

来源：

PROCEEDINGS OF THE 2023 THE 22ND INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS, IPSN 2023 | 2023年

关键词：

Millimeter Wave; Point Cloud Data; Deep Learning;

D O I：

10.1145/3583120.3589822

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：308 / 309

页数：2