CORF-GS：通过耦合光学-射频高斯格点法进行实时无线辐射场重建
CORF-GS: Real-Time Wireless Radiance Field Reconstruction via Coupled Optical-RF Gaussian Splatting
AI总结：
提出CORF-GS实时无线辐射场重建框架，通过处理光学和射频关键帧，构建统一高斯表示，利用光学引导采样和耦合优化，实现了高射频频谱合成质量并大幅减少重建时间。
AI中文摘要：
基于3D高斯格点法的无线辐射场（WRF）重建的最新进展为无线信道建模提供了有效解决方案。然而，现有WRF重建方法依赖预先收集的观测数据和离线优化，难以提供实时信道信息。为此提出CORF-GS实时WRF重建框架，处理连续的光学和射频关键帧。它为光学和射频构建统一高斯表示，利用光学引导高斯采样在稀疏区域 densify WRF，并进行耦合光学-射频优化。模拟表明，CORF-GS实现了最优射频频谱合成质量，与现有方法相比重建时间减少了6.4倍。
英文摘要：
Recent advances in 3D Gaussian Splatting (3DGS)-based wireless radiance field (WRF) reconstruction provide an efficient solution for wireless channel modeling. However, existing WRF reconstruction methods rely on pre-collected observations and offline optimization, and thus struggle to provide real-time channel knowledge. To bridge this gap, we propose CORF-GS, a real-time WRF reconstruction framework that processes sequential optical and radio frequency (RF) keyframes. Specifically, CORF-GS constructs a unified Gaussian representation for optical and RF with shared geometry and modality-specific appearance, allowing high-resolution optical images to provide structural priors for WRF reconstruction. When a new keyframe arrives, CORF-GS first employs optical-guided Gaussian sampling to densify the WRF in under-represented regions. Since light and radio waves may respond differently to the same object surfaces due to wavelength mismatch, relying solely on optical guidance may neglect RF-informative areas. Therefore, CORF-GS performs coupled optical-RF optimization to jointly refine the shared Gaussians. Compared with the existing two-stage training pipelines, this prevents WRF from passively adapting to a frozen optical geometry and encourages the shared Gaussians to adapt to both optical structures and RF power distributions. Simulations show that CORF-GS achieves state-of-the-art RF spectrum synthesis quality and reduces the reconstruction time by $6.4\times$ compared with existing WRF methods.
