# Neural Reflectance Fields for Radio-Frequency Ray Tracing

arXiv: https://arxiv.org/abs/2501.02458 · HTML: https://mlsanity.com/paper/2501.02458/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation](https://mlsanity.com/paper/2303.17147/) (2023)
- [NeRF2: Neural Radio-Frequency Radiance Fields](https://mlsanity.com/paper/2305.06118/) (2023)
- [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024)

## Built on by (most cited first, 3 of 3)

- [Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis](https://mlsanity.com/paper/2502.05708/) (2025)
- [A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction](https://mlsanity.com/paper/2604.11983/) (2026)
- [Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction](https://mlsanity.com/paper/2604.25945/) (2026)

## Abstract (arXiv)

Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and surface material properties. The advances in computer vision and LiDAR make scene geometry estimation increasingly accurate, but there still lacks scalable and efficient approaches to estimate the material reflectivity in real-world environment. In this work, we tackle this problem by learning the material reflectivity efficiently from the path loss of the RF signal from the transmitters to receivers. Specifically, we want the learned material reflection coefficients to minimize the gap between the predicted and measured powers of the receivers. We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects. We further propose a differentiable RF ray tracing framework that optimizes the neural reflectance field to match the signal strength measurements. We simulate a complex real-world environment for experiments and our simulation results show that the neural reflectance field can successfully learn the reflection coefficients for all incident angles. As a result, our approach achieves better accuracy in predicting the powers of receivers with significantly less training data compared to existing approaches.