Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911514720468992 |
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| author | Ninkovic, Vukan Sobot, Tamara Vincan, Vladimir Gojic, Gorana Miskovic, Dragisa Vukobratovic, Dejan |
| author_facet | Ninkovic, Vukan Sobot, Tamara Vincan, Vladimir Gojic, Gorana Miskovic, Dragisa Vukobratovic, Dejan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_13560 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness Ninkovic, Vukan Sobot, Tamara Vincan, Vladimir Gojic, Gorana Miskovic, Dragisa Vukobratovic, Dejan Signal Processing Machine Learning 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. |
| title | Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2603.13560 |