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

TL;DR

This paper introduces a semantic communication framework for wireless 3D point cloud transmission, showing that task-level inference remains robust despite geometric reconstruction degradation under channel noise.

Contribution

It presents a novel end-to-end semantic communication system for 3D point clouds and systematically compares geometric and semantic robustness under wireless channel impairments.

Findings

Semantic inference remains stable across various SNR levels.

Geometric quality degrades significantly with noise, but task accuracy does not.

Task-oriented transmission can reduce bandwidth needs without sacrificing performance.

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.