Recent advances in sensing and computer vision (CV) technologies have opened the door for the application of deep learning (DL)-based CV technologies in the realm of 6G wireless communications. For the successful application of this emerging technology, it is crucial to have a qualified vision dataset tailored for wireless applications (e.g., RGB images containing wireless devices such as laptops and cell phones). An aim of this paper is to propose a large-scale vision dataset referred to as Vision Objects for Millimeter and Terahertz Communications (VOMTC). The VOMTC dataset consists of 20,232 pairs of RGB and depth images obtained from a camera attached to the base station (BS), with each pair labeled with three representative object categories (person, cell phone, and laptop) and bounding boxes of the objects. Through experimental studies of the VOMTC datasets, we show that the beamforming technique exploiting the VOMTC-trained object detector outperforms conventional beamforming techniques.

本研究针对6G无线通信中缺乏专用视觉数据集的问题，提出了一个大型视觉数据集VOMTC，包含20232对RGB和深度图像，并标注了三种代表性目标类别。实验表明，利用该数据集训练的目标检测器在波束形成技术上优于传统技术，为无线通讯的深度学习应用提供了新的解决方案。

毫米波和太赫兹通信的视觉对象