# 面向实时移动3D重建的语义通信

> 原文链接：https://robotics.hellobike.com/papers/2026-07-17-2607-16128
> 频道：papers | 更新日期：2026-07-17 | 标签：arxiv, cs.RO
> 核心摘要：面向实时移动3D重建，提出带像素置信图的语义通信框架，并在仿真中验证其提升位姿估计与结构一致性。

# 面向实时移动3D重建的语义通信

::: v-pre
> 原文链接：[Toward Semantic Communication for Real-time Mobile 3D Reconstruction](https://arxiv.org/abs/2607.16128)（arXiv 2607.16128 · 发布日期：2026-07-17）

**Toward Semantic Communication for Real-time Mobile 3D Reconstruction**

面向实时移动3D重建，提出带像素置信图的语义通信框架，并在仿真中验证其提升位姿估计与结构一致性。

## 核心要点

- 实时移动3D重建需在线估计位姿与几何，多视角一致性易受通信失真影响。
- 框架用语义收发器重建图像，并输出逐像素置信图表示区域可靠性。
- 将置信度融入基于RANSAC的位姿初始化和束调整，降低不可靠区域影响。
- 仿真显示，相比现有SemCom和传统分离源信道编码，图像质量保持较高且位姿与结构更稳健。

**验证范围与局限**：验证范围为仿真中的实时移动3D重建与噪声信道。

## 英文摘要 (Abstract)

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.

作者：Fangzhou Zhao, Yao Sun, Xuesong Liu, Runze Cheng, Shang Kai, Yi Sun
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## 原始参考来源 (Sources)

- [arXiv 2607.16128：Toward Semantic Communication for Real-time Mobile 3D Reconstruction](https://arxiv.org/abs/2607.16128)