Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction
Chaozheng Wen, Jingwen Tong, Zehong Lin, Chenghong Bian, Jun Zhang

TL;DR
This paper introduces URF-GS, a unified framework that combines visual and wireless data to construct accurate 3D radio maps, enhancing environmental understanding for wireless systems.
Contribution
It presents a novel unified radiation field model based on 3D Gaussian splatting and inverse rendering, enabling cross-modal data fusion for improved radio map accuracy.
Findings
Achieves up to 24.7% improvement in spatial spectrum accuracy.
Provides a 10x increase in sample efficiency over NeRF-based methods.
Successfully applied in Wi-Fi deployment and robot path planning.
Abstract
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…
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