Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurate representation of the environment, leading to context-aware communications. Some widely equipped sensors such as cameras and RADAR sensors can provide some environmental perceptions. However, they are not enough to generate precise environmental representations, especially in adverse weather conditions. On the other hand, the LiDAR sensors provide more accurate representations, however, their widespread adoption is hindered by their high cost. This paper proposes a novel approach to enhance the wireless communication systems by synthesizing LiDAR point clouds from images and RADAR data. Specifically, it uses a multimodal transformer architecture and pre-trained encoding models to enable an accurate LiDAR generation. The proposed framework is evaluated on the DeepSense 6G dataset, which is a real-world dataset curated for context-aware wireless applications. Our results demonstrate the efficacy of the proposed approach in accurately generating LiDAR point clouds. We achieve a modified mean squared error of 10.3931. Visual examination of the images indicates that our model can successfully capture the majority of structures present in the LiDAR point cloud for diverse environments. This will enable the base stations to achieve more precise environmental sensing. By integrating LiDAR synthesis with existing sensing modalities, our method can enhance the performance of various wireless applications, including beam and blockage prediction.
Multimodal Signal Processing For Thermo-Visible-Lidar Fusion In Real-time 3D Semantic Mapping
College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机械与控制工程学院）；School of Robotics, Xi’an-Jiaotong Liverpool University（西安交通大学利物浦大学机器人学院）
PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing（浙江省多模态通信网络与智能信息处理重点实验室）
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Software, Shandong University（山东大学软件学院）；School of Engineering and Sustainable Development, De Montfort University（德蒙福特大学工程与可持续发展学院）
Semantics-Guided Multimodal Masked Autoencoder Pretraining for 3D BEV Object Detection
University of Lincoln, Lincoln Centre for Autonomous Systems（林肯大学，林肯自主系统中心）；University of Cambridge, Institute for Manufacturing, Department of Engineering（剑桥大学，制造研究所，工程系）
AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；DarkMatter AI Research（DarkMatter AI研究）；University of Oxford（牛津大学）；HKU（香港大学）；Cainiao Group（菜鸟集团）；NCUT（北方工业大学）；Sun Yat-sen University（中山大学）；Westlake University（西湖大学）
VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model
University of Chinese Academy of Sciences（中国科学院大学）；Baidu Research（百度研究院）；University of Michigan（密歇根大学）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学与应用研究院）
LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detection
School of Computer Science and Technology, Tongji University, Shanghai 201804, China（计算机科学与技术学院，同济大学，上海）；School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China（电子信息工程学院，同济大学，上海）
Toward Interpretable Multimodal Fusion: Heat Conduction Modeling for Hyperspectral and LiDAR Joint Classification
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Key Laboratory of Target Cognition and Application Technology（目标认知与应用技术重点实验室）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院遥感与数字地球国家重点实验室）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Faculty of Electrical and Computer Engineering, University of Iceland（冰岛大学电气与计算机工程系）
Chat-to-Design: AI Assisted Personalized Fashion Design
Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；The Chinese University of Hong Kong（香港中文大学）；Wuhan University of Technology（武汉理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation
NetEase, Inc.（网易公司）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；City University of Hong Kong（香港城市大学）；Independent Researcher & Technical Artists（独立研究者及技术艺术家）
Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing University of Chemical Technology（北京化工大学）；University of Science and Technology Beijing（北京科技大学）；North China University of Technology（北方工业大学）
