mlsanityData to 14 Oct 2026

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

arXiv 2603.13560 ↗First posted 13 Mar 2026eess.SP cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 13 Mar 2026.

Builds on

12 of 22 references.

References · 12

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2020Point Transformercited by 1,470
  5. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  6. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  7. 2017An Introduction to Deep Learning for the Physical Layercited by 233
  8. 2020Semantics-Empowered Communication for Networked Intelligent Systemscited by 63
  9. 2020Review: deep learning on 3D point cloudscited by 29
  10. 2023Semantic-aware Transmission for Robust Point Cloud Classificationcited by 4
  11. 2024Point Cloud in the Aircited by 4
  12. 2025Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmissioncited by 1

Built on by

Not cited yet.