Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness

• URL: http://arxiv.org/abs/2603.13560v1
• Date: Fri, 13 Mar 2026 19:55:15 GMT
• Title: Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness
• Abstract summary: We propose an end-to-end semantic communication framework for wireless 3D PC transmission.<n>We show that semantic inference remains stable across a broad signal-to-noise ratio range even when geometric reconstruction quality degrades significantly.
• Score: 1.7445917991523536
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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