# VOMTC: Vision Objects for Millimeter and Terahertz Communications

arXiv: https://arxiv.org/abs/2409.09330 · HTML: https://mlsanity.com/paper/2409.09330/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction](https://mlsanity.com/paper/1911.06255/) (2019)
- [A Survey of Beam Management for mmWave and THz Communications Towards 6G](https://mlsanity.com/paper/2308.02135/) (2023)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction](https://mlsanity.com/paper/2003.00645/) (2020)
- [LIDAR Data for Deep Learning-Based mmWave Beam-Selection](https://mlsanity.com/paper/1908.07488/) (2019)
- [Computer Vision Aided mmWave Beam Alignment in V2X Communications](https://mlsanity.com/paper/2207.11409/) (2022)
- [Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming](https://mlsanity.com/paper/2209.02258/) (2022)
- [Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks](https://mlsanity.com/paper/1803.09698/) (2018)
- [Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges](https://mlsanity.com/paper/2006.05782/) (2020)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems](https://mlsanity.com/paper/2101.08469/) (2021)
- [Role of Sensing and Computer Vision in 6G Wireless Communications](https://mlsanity.com/paper/2405.03945/) (2024)
- [Beamforming Technologies for Ultra-Massive MIMO in Terahertz Communications](https://mlsanity.com/paper/2107.03032/) (2021)
- [Veni Vidi Dixi: Reliable Wireless Communication with Depth Images](https://mlsanity.com/paper/1912.01879/) (2019)
- [Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning](https://mlsanity.com/paper/2109.14686/) (2021)
- [LiDAR Aided Human Blockage Prediction for 6G](https://mlsanity.com/paper/2110.00349/) (2021)
- [Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results](https://mlsanity.com/paper/2207.05032/) (2022)
- [Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs](https://mlsanity.com/paper/2203.16419/) (2022)

## Built on by (most cited first, 3 of 3)

- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [CONVERGE: A Multi-Agent Vision-Radio Architecture for xApps](https://mlsanity.com/paper/2508.04556/) (2025)
- [Deep Learning and Matrix Completion-aided IoT Network Localization in the Outlier Scenarios](https://mlsanity.com/paper/2508.18225/) (2025)

## Abstract (arXiv)

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.