HiFiRadio: High-Fidelity Radio Map Reconstruction for 3D Real-World Scenes

The reconstruction of high-fidelity radio maps is pivotal for wireless network planning but remains challenging due to the tension between physical accuracy and computational efficiency. We propose HiFiRadio, a novel framework that achieves a breakthrough in this balance by integrating centimeter-resolution 3D environmental meshes with semantic-aware propagation modeling. At its core, HiFiRadio introduces a semantic-enhanced 3D indexing structure that efficiently manages complex terrain data, enabling real-time classification of signal paths into line-of-sight, non-line-of-sight, and vegetation-obstructed categories. This classification directly guides a hybrid propagation model, which dynamically applies dedicated loss calculations for buildings and foliage, grounded in physical principles. Extensive experiments demonstrate that HiFiRadio attains an accuracy comparable to commercial ray-tracing tools while being orders of magnitude faster. It also significantly outperforms existing learning-based baselines in both accuracy and scalability, a claim further validated by field measurements. By making high-fidelity, real-time radio map reconstruction practical for large-scale scenes, HiFiRadio establishes a new state of the art with immediate applications in network planning, UAV pathing, and dynamic spectrum access.