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SGFormer

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Satellite-Ground Fusion for 3D Semantic Scene Completion

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<h1 align="center">SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion [CVPR'25]</h1>
 <h3 align="center"><a href="https://zju3dv.github.io/sgformer/">🌐Project page</a> | <a href="https://www.arxiv.org/abs/2503.16825">📝Paper</a></h3>
 
 <a href="https://github.com/gxytcrc/">Xiyue Guo1</a>
 ·
 <a href="https://github.com/hjr37/">Jiarui Hu1</a>
 ·
 <a href="https://github.com/JunjH/">Junjie Hu2</a>
 ·
 <a href="http://www.cad.zju.edu.cn/home/bao/">Hujun Bao1</a>
 ·
 <a href="http://www.cad.zju.edu.cn/home/gfzhang/">Guofeng Zhang1*</a>
 
 1 State Key Lab of CAD&CG, Zhejiang University,
 2 Chinese Universty of Hangkang, Shenzhen 
 * Corresponding author.

This is the official implementation of SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion. SGFormer is the first satellite-ground cooperative SSC framework that achieves state-of-the-art performance in scene semantic completion.

# Training
Download the pretrained weight of the satellite backbone [Semantic-KITTI: Google Drive](https://drive.google.com/file/d/1qjv9dLFNdn_fJ9a2MOxBfwMrhhX9E6Y6/view?usp=drive_link)

```shell
python train.py 
```

# Eval
Download the weight of our model [Semantic-KITTI: Google Drive](https://drive.google.com/file/d/1UZ6YnTw26JzWdgxiqbNF8yV3LCB1jBzD/view?usp=drive_link)
```shell
python eval.py 
```
# Visualization Result
 <a href="">
 <img src="https://github.com/gxytcrc/fictional-succotash/blob/main/vis.jpg" alt="SGFormer pipeline" width="100%">
 </a>

# News
- [x] 2025.02.26 --- Our paper has been accepted at CVPR 2025！  
- [x] 2025.04.08 --- We have updated the `README.md` and are preparing to open-source our code！
- [x] 2025.08.25 --- We have update our code

SGFormer

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