发表机构
School of Electrical and Electronic Engineering, North China Electric Power University; James Watt School of Engineering, University of Glasgow; School of Computer Science and Technology, China University of Petroleum (East China)（华北电力大学电气与电子工程学院; 格拉斯哥大学詹姆斯·瓦特工程学院; 中国石油大学（华东）计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对实时移动 3D 重建中几何估计对通信失真敏感的问题，提出语义通信框架，含语义收发器输出带置信度图的重建图像，引入置信度引导几何估计方法，仿真显示该框架能保持图像质量并提升姿态估计精度和 3D 结构一致性。
AI 中文摘要
实时移动 3D 重建对许多新兴应用至关重要，移动平台持续捕获图像流并传输到计算服务器进行场景理解。与离线重建不同，采集时需实时估计相机姿态和场景几何，多视图一致性成为实时要求，且几何估计对通信失真敏感。语义通信可传输紧凑语义信息，但现有设计在图像或单视图级别优化，未为几何估计提供明确可靠性信息。在此背景下，我们提出用于实时移动 3D 重建的语义通信框架。该框架包括语义收发器，输出重建图像及像素级置信度图以量化各区域可靠性。还引入置信度引导的几何估计方法，将置信度纳入基于 RANSAC 的姿态初始化和光束平差法，减少不可靠区域影响，增强在噪声信道下的鲁棒性。仿真表明，与现有语义通信和传统分离信源与信道编码相比，我们的框架保持高图像质量，同时显著提高姿态估计精度和 3D 结构一致性。
英文摘要
Real-time mobile 3D reconstruction is fundamental to many emerging applications such as autonomous navigation and digital twin construction, where a moving platform continuously captures an image stream and transmit to a computing server for scene understanding. Unlike offline reconstruction, camera poses and scene geometry are estimated on-the-fly during acquisition, making multi-view consistency a real-time requirement and rendering geometric estimation highly sensitive to communication-induced distortions. Semantic communication (SemCom) transmits compact semantic information, offering a promising way to preserve task-critical data over unreliable links. However, existing designs are optimized at the image or single-view level and without providing explicit reliability information for geometric estimation, limiting their applicability to real-time mobile 3D reconstruction. In this context, we propose a SemCom framework for real-time mobile 3D reconstruction. The framework includes a semantic transceiver that outputs a reconstructed image alongside a pixel-wise confidence map, quantifying the reliability of each region. We further introduce a confidence-guided geometric estimation method, incorporating confidence into RANSAC-based pose initialization and bundle adjustment to reduce the influence of unreliable regions and enhance robustness under noisy channels. Simulations show that, compared to existing SemCom and traditional seperate source and channel coding, our framework maintains high image quality while significantly improving pose estimation accuracy and 3D structural consistency.
