无线传输3D点云时，语义稳定比几何精确更重要。
Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness
- 设计端到端框架，共享传输表示同时支持几何重建与分类任务。
- 实测显示：信噪比下降时，分类准确率远高于几何失真程度。
- 适合工业场景中带宽受限的感知系统设计，强调任务可靠性。
高维3D点云的无线传输在工业协作机器人系统中日益重要。传统压缩方法侧重几何保真度，但许多实际应用依赖于可靠的任务级推断而非坐标精确重建。本文提出一种面向任务的3D点云无线通信端到端框架，并系统研究了在信道干扰下几何重建质量与语义鲁棒性的关系。所提架构可从共享传输表示中联合恢复几何结构与执行物体分类，实现坐标级与任务级对噪声敏感性的直接对比。在真实工业数据集上的实验表明存在显著不对称性：即使几何重建质量大幅下降，语义推断仍能在较宽信噪比（SNR）范围内保持稳定。结果表明，可靠的任务执行并不需要高保真的几何还原，为带宽与功耗受限的工业环境中的任务导向无线感知系统提供了设计启示。
原文摘要 · Abstract (English)
Wireless transmission of high-dimensional 3D point clouds (PCs) is increasingly required in industrial collaborative robotics systems. Conventional compression methods prioritize geometric fidelity, although many practical applications ultimately depend on reliable task-level inference rather than exact coordinate reconstruction. In this paper, we propose an end-to-end semantic communication framework for wireless 3D PC transmission and conduct a systematic study of the relationship between geometric reconstruction fidelity and semantic robustness under channel impairments. The proposed architecture jointly supports geometric recovery and object classification from a shared transmitted representation, enabling direct comparison between coordinate-level and task-level sensitivity to noise. Experimental evaluation on a real industrial dataset reveals a pronounced asymmetry: semantic inference remains stable across a broad signal-to-noise ratio (SNR) range even when geometric reconstruction quality degrades significantly. These results demonstrate that reliable task execution does not require high-fidelity geometric recovery and provide design insights for task-oriented wireless perception systems in bandwidth- and power-constrained industrial environments.
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