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

arXiv: https://arxiv.org/abs/2603.13560 · HTML: https://mlsanity.com/paper/2603.13560/

## 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 (most cited first, 12 of 12)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Review: deep learning on 3D point clouds](https://mlsanity.com/paper/2001.06280/) (2020)
- [Semantic-aware Transmission for Robust Point Cloud Classification](https://mlsanity.com/paper/2306.13296/) (2023)
- [Point Cloud in the Air](https://mlsanity.com/paper/2401.00658/) (2024)
- [Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission](https://mlsanity.com/paper/2506.03211/) (2025)

## 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.