# Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis

arXiv: https://arxiv.org/abs/2502.05708 · HTML: https://mlsanity.com/paper/2502.05708/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- Builds on 26 of its 68 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023)
- [Neural Rays for Occlusion-aware Image-based Rendering](https://mlsanity.com/paper/2107.13421/) (2021)
- [GRF: Learning a General Radiance Field for 3D Representation and Rendering](https://mlsanity.com/paper/2010.04595/) (2020)
- [GNeRF: GAN-based Neural Radiance Field without Posed Camera](https://mlsanity.com/paper/2103.15606/) (2021)
- [Generative Neural Articulated Radiance Fields](https://mlsanity.com/paper/2206.14314/) (2022)
- [Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation](https://mlsanity.com/paper/2104.08771/) (2021)
- [MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos](https://mlsanity.com/paper/2212.13056/) (2022)
- [NeRF2: Neural Radio-Frequency Radiance Fields](https://mlsanity.com/paper/2305.06118/) (2023)
- [NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction](https://mlsanity.com/paper/2403.03241/) (2024)
- [GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images](https://mlsanity.com/paper/2303.13777/) (2023)
- [WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields](https://mlsanity.com/paper/2308.04826/) (2023)
- [RF-3DGS: Wireless Channel Modeling with Radio Radiance Field and 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19420/) (2024)
- [GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding](https://mlsanity.com/paper/2403.03608/) (2024)
- [GSRF: Complex-Valued 3D Gaussian Splatting for Efficient Radio-Frequency Data Synthesis](https://mlsanity.com/paper/2502.01826/) (2025)
- [CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering](https://mlsanity.com/paper/2311.15510/) (2023)
- [Radio Frequency Ray Tracing with Neural Object Representation](https://mlsanity.com/paper/2411.18635/) (2024)
- [Neural Reflectance Fields for Radio-Frequency Ray Tracing](https://mlsanity.com/paper/2501.02458/) (2025)

## Built on by (most cited first, 2 of 2)

- [SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning](https://mlsanity.com/paper/2511.05482/) (2025)
- [Radiance-Field Guided Pretraining: Scaling Localization Models with Unlabeled Wireless Signals](https://mlsanity.com/paper/2512.07309/) (2025)

## Abstract (arXiv)

We present GRaF, Generalizable Radio-Frequency (RF) Radiance Fields, a framework that models RF signal propagation to synthesize spatial spectra at arbitrary transmitter or receiver locations, where each spectrum measures signal power across all surrounding directions at the receiver. Unlike state-of-the-art methods that adapt vanilla Neural Radiance Fields (NeRF) to the RF domain with scene-specific training, GRaF generalizes across scenes to synthesize spectra. To enable this, we prove an interpolation theory in the RF domain: the spatial spectrum from a transmitter can be approximated using spectra from geographically proximate transmitters. Building on this theory, GRaF comprises two components: (i) a geometry-aware Transformer encoder that captures spatial correlations from neighboring transmitters to learn a scene-independent latent RF radiance field, and (ii) a neural ray tracing algorithm that estimates spectrum reception at the receiver. Experimental results demonstrate that GRaF outperforms existing methods on single-scene benchmarks and achieves state-of-the-art performance on unseen scene layouts.