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

2026-03-13

AI Summary

This work addresses the challenge of wirelessly transmitting high-dimensional 3D point clouds in industrial collaborative robotic systems, where bandwidth and power constraints limit conventional approaches that prioritize geometric fidelity over task-relevant semantic reliability. To bridge this gap, we propose an end-to-end semantic communication framework that jointly optimizes geometric reconstruction and object classification by leveraging shared representation learning and realistic channel modeling. Our analysis reveals that semantic performance remains robust even under severe channel impairments that drastically degrade geometric quality. Experimental results demonstrate that the proposed method maintains high semantic accuracy across a wide signal-to-noise ratio (SNR) range, challenging the assumption that high-fidelity geometry is essential for reliable task execution and thereby advancing a task-oriented paradigm for wireless perception.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• 3D point clouds
• wireless transmission
• semantic robustness
• geometric fidelity
• task-oriented communication

Innovation

• semantic communication
• 3D point cloud
• task-oriented transmission
• geometric fidelity
• semantic robustness