RadCloudSplat

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Official implementation of “RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation,” IEEE INFOCOM 2026. Camera-free radio-frequency Gaussian splatting for radiomap and RSS extrapolation with LiDAR point-cloud priors.

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# RadCloudSplat

**Official implementation** of:
<h3><a href="">RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation</a></h3>

[Yiheng Wang](https://yeehengwang.github.io/), [Ye Xue](https://yokoxue.github.io/), Shutao Zhang, Hongmiao Fan and [Tsung-Hui Chang](https://myweb.cuhk.edu.cn/changtsunghui/Home)

Thanks for your interest in our work. This repository contains code and links to the **RadCloudSplat** method for radiomap extrapolation, which has been accepted by **IEEE INFOCOM 2026**.

More information can be refered to:
- **Paper:** [arXiv](https://arxiv.org/abs/2502.12686)
- **Published version:** [IEEE Xplore](https://ieeexplore.ieee.org/document/11571385)
- **Code:** This repository
- **Project page:** [Project Page](https://yeehengwang.github.io/RadCloudSplat/)
- **License:** MIT

## Introduction
In this work, we first extended 3DGS to the radio frequency domain, leveraging **camera-free RadCloudSplat** to extrapolate RSSs with high accuracy from sparse measurements in an outdoor environment. By efficiently selecting the means of key virtual scatterers from dense point clouds aided by the **relaxed-mean (RM) scheme**, the model captured **intricate multi-path propagation characteristics**. Experiments and analysis validated the effectiveness of these scatterers, advancing the state-of-the-art in wireless network modeling and extrapolation performance and highlighting the transformative potential of integrating advanced 3D modeling techniques with wireless propagation analysis for next-generation applications in the radio domain.

Schematic illustration of **RadCloudSplat**, comprising three major parts: 1) Relaxed-Mean Reparameterization for Key Virtual Scatters Positions Extraction. 2) Camera-Free RadCloudSplat Model for RSS Synthesis. 3) Optimizing RadCloudSplat Scheme

![](https://raw.githubusercontent.com/yeehengwang/RadCloudSplat/main/assets/arch.png)

## News
Due to copyright issues regarding the measurement real data from wireless network, we are unable to provide the data used in the paper.

- [x] Release the demo dataset revised from NeRF2 datasets. 
- [x] Release the training code. 
- [x] Release the inference code. 
- [x] Release the paper of RadCloudSplat on [arXiv](https://arxiv.org/abs/2502.12686).

## Training & Evaluation
A small demo dataset in ./demo_data is included to help quickly verify the code, which can be executed using the following command:
```python
python train_radcloudsplat.py
```
More datasets can be found [here](https://github.com/XPengZhao/NeRF2?tab=readme-ov-file).<be>

When you finish the training, you can inference the trained model by using the following command:
```python
python train_radcloudsplat.py --mode test
```

## Citation

If you find our work useful in your research, please consider citing RadCloudSplat:

```tex
@inproceedings{wang2026radcloudsplat,
  title={RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation},
  author={Wang, Yiheng and Xue, Ye and Zhang, Shutao and Fan, Hongmiao and Chang, Tsung-Hui},
  booktitle={IEEE INFOCOM 2026-IEEE Conference on Computer Communications},
  pages={1--10},
  year={2026},
  organization={IEEE}
}
```

## Acknowledgement

We thank Dr. Xiaopeng Zhao, the authors of [NeRF2](https://github.com/XPengZhao/NeRF2), for making their code and dataset available.

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