# GSRF: Complex-Valued 3D Gaussian Splatting for Efficient Radio-Frequency Data Synthesis

arXiv: https://arxiv.org/abs/2502.01826 · HTML: https://mlsanity.com/paper/2502.01826/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 11 of 11)

- [Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis](https://mlsanity.com/paper/2502.05708/) (2025)
- [Wideband RF Radiance Field Modeling Using Frequency-embedded 3D Gaussian Splatting](https://mlsanity.com/paper/2505.20714/) (2025)
- [RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting](https://mlsanity.com/paper/2508.16849/) (2025)
- [GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels](https://mlsanity.com/paper/2511.22793/) (2025)
- [Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds](https://mlsanity.com/paper/2607.28994/) (2026)
- [SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning](https://mlsanity.com/paper/2511.05482/) (2025)
- [Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis](https://mlsanity.com/paper/2605.07781/) (2026)
- [PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting](https://mlsanity.com/paper/2605.08035/) (2026)
- [HybridSim: A Physics-Learning Hybrid Digital Twin for mmWave Human Sensing](https://mlsanity.com/paper/2607.15806/) (2026)
- [CORF-GS: Real-Time Wireless Radiance Field Reconstruction via Coupled Optical-RF Gaussian Splatting](https://mlsanity.com/paper/2607.25569/) (2026)
- [Physics-Unrolled Neural Operator for Wireless Field Modeling](https://mlsanity.com/paper/2608.18495/) (2026)

## Abstract (arXiv)

Synthesizing radio-frequency (RF) data given the transmitter and receiver positions, e.g., received signal strength indicator (RSSI), is critical for wireless networking and sensing applications, such as indoor localization. However, it remains challenging due to complex propagation interactions, including reflection, diffraction, and scattering. State-of-the-art neural radiance field (NeRF)-based methods achieve high-fidelity RF data synthesis but are limited by long training times and high inference latency. We introduce GSRF, a framework that extends 3D Gaussian Splatting (3DGS) from the optical domain to the RF domain, enabling efficient RF data synthesis. GSRF realizes this adaptation through three key innovations: First, it introduces complex-valued 3D Gaussians with a hybrid Fourier-Legendre basis to model directional and phase-dependent radiance. Second, it employs orthographic splatting for efficient ray-Gaussian intersection identification. Third, it incorporates a complex-valued ray tracing algorithm, executed on RF-customized CUDA kernels and grounded in wavefront propagation principles, to synthesize RF data in real time. Evaluated across various RF technologies, GSRF preserves high-fidelity RF data synthesis while achieving significant improvements in training efficiency, shorter training time, and reduced inference latency.