Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914515979862016 |
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| author | Wen, Chaozheng Tong, Jingwen Lin, Zehong Bian, Chenghong Zhang, Jun |
| author_facet | Wen, Chaozheng Tong, Jingwen Lin, Zehong Bian, Chenghong Zhang, Jun |
| contents | The emerging applications of next-generation wireless networks demand high-fidelity environmental intelligence. 3D radio maps bridge physical environments and electromagnetic propagation for spectrum planning and environment-aware sensing. However, most existing methods treat visual and wireless data as independent modalities and fail to leverage shared electromagnetic propagation principles. To bridge this gap, we propose URF-GS, a unified radio-optical radiation field framework based on 3D Gaussian splatting and inverse rendering for 3D radio map construction. By fusing cross-modal observations, our method recovers scene geometry and material properties to predict radio signals under arbitrary transceiver configurations without retraining. Experiments demonstrate up to a 24.7% improvement in spatial spectrum accuracy and a 10x increase in sample efficiency compared with NeRF-based methods. We further showcase URF-GS in Wi-Fi AP deployment and robot path planning tasks. This unified visual-wireless representation supports holistic radiation field modeling for future wireless communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19216 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction Wen, Chaozheng Tong, Jingwen Lin, Zehong Bian, Chenghong Zhang, Jun Networking and Internet Architecture Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning The emerging applications of next-generation wireless networks demand high-fidelity environmental intelligence. 3D radio maps bridge physical environments and electromagnetic propagation for spectrum planning and environment-aware sensing. However, most existing methods treat visual and wireless data as independent modalities and fail to leverage shared electromagnetic propagation principles. To bridge this gap, we propose URF-GS, a unified radio-optical radiation field framework based on 3D Gaussian splatting and inverse rendering for 3D radio map construction. By fusing cross-modal observations, our method recovers scene geometry and material properties to predict radio signals under arbitrary transceiver configurations without retraining. Experiments demonstrate up to a 24.7% improvement in spatial spectrum accuracy and a 10x increase in sample efficiency compared with NeRF-based methods. We further showcase URF-GS in Wi-Fi AP deployment and robot path planning tasks. This unified visual-wireless representation supports holistic radiation field modeling for future wireless communication systems. |
| title | Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction |
| topic | Networking and Internet Architecture Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2601.19216 |