---
license: mit
metrics:
- accuracy
base_model:
- facebook/VGGT_tracker_fixed
pipeline_tag: image-to-3d
---
<div align="center">
<h2>⚡️ FastVGGT: Training-Free Acceleration of Visual Geometry Transformer</h2>
  
<p align="center">
  <a href="https://arxiv.org/abs/2509.02560"><img src="https://img.shields.io/badge/arXiv-FastVGGT-red?logo=arxiv" alt="Paper PDF"></a>
  <a href="https://mystorm16.github.io/fastvggt/"><img src="https://img.shields.io/badge/Project_Page-FastVGGT-yellow" alt="Project Page"></a>
</p>
  

[You Shen](https://mystorm16.github.io/), [Zhipeng Zhang](https://zhipengzhang.cn/), [Yansong Qu](https://quyans.github.io/), [Liujuan Cao](https://mac.xmu.edu.cn/ljcao/)
</div>


## 🔭 Overview

FastVGGT observes **strong similarity** in attention maps and leverages it to design a training-free acceleration method for long-sequence 3D reconstruction, **achieving up to 4× faster inference without sacrificing accuracy.**


## ⚙️ Environment Setup
First, create a virtual environment using Conda, clone this repository to your local machine, and install the required dependencies.


```bash
conda create -n fastvggt python=3.10
conda activate fastvggt
git clone git@github.com:mystorm16/FastVGGT.git
cd FastVGGT
pip install -r requirements.txt
```

Next, prepare the ScanNet dataset: http://www.scan-net.org/ScanNet/

Then, download the VGGT checkpoint (we use the checkpoint link provided in https://github.com/facebookresearch/vggt/tree/evaluation/evaluation):
```bash
wget https://huggingface.co/facebook/VGGT_tracker_fixed/resolve/main/model_tracker_fixed_e20.pt
```

Finally, configure the dataset path and VGGT checkpoint path. For example:
```bash
    parser.add_argument(
        "--data_dir", type=Path, default="/data/scannetv2/process_scannet"
    )
    parser.add_argument(
        "--gt_ply_dir",
        type=Path,
        default="/data/scannetv2/OpenDataLab___ScanNet_v2/raw/scans",
    )
    parser.add_argument(
        "--ckpt_path",
        type=str,
        default="./ckpt/model_tracker_fixed_e20.pt",
    )
```


## 💎 Observation

Note: A large number of input_frames may significantly slow down saving the visualization results. Please try using a smaller number first.
```bash
python eval/eval_scannet.py --input_frame 30 --vis_attn_map --merging 0
```

We observe that many token-level attention maps are highly similar in each block, motivating our optimization of the Global Attention module.



## 🏀 Evaluation
### Custom Dataset
Please organize the data according to the following directory:
```
<data_path>/
├── images/       
│   ├── 000000.jpg
│   ├── 000001.jpg
│   └── ...
├── pose/                # Optional: Camera poses
│   ├── 000000.txt 
│   ├── 000001.txt
│   └── ...
└── gt_ply/              # Optional: GT point cloud
    └── scene_xxx.ply   
```
- Required: `images/`
- Additionally required when `--enable_evaluation` is enabled: `pose/` and `gt_ply/`

Inference only:

```bash
python eval/eval_custom.py \
  --data_path /path/to/your_dataset \
  --output_path ./eval_results_custom \
  --plot
```

Inference + Evaluation (requires `pose/` and `gt_ply/`):

```bash
python eval/eval_custom.py \
  --data_path /path/to/your_dataset \
  --enable_evaluation \
  --output_path ./eval_results_custom \
  --plot
```

### ScanNet
Evaluate FastVGGT on the ScanNet dataset with 1,000 input images. The **--merging** parameter specifies the block index at which the merging strategy is applied:

```bash
python eval/eval_scannet.py --input_frame 1000 --merging 0
```

Evaluate Baseline VGGT on the ScanNet dataset with 1,000 input images:
```bash
python eval/eval_scannet.py --input_frame 1000
```

### 7 Scenes & NRGBD
Evaluate across two datasets, sampling keyframes every 10 frames:
```bash
python eval/eval_7andN.py --kf 10
```

## 🍺 Acknowledgements

- Thanks to these great repositories: [VGGT](https://github.com/facebookresearch/vggt), [Dust3r](https://github.com/naver/dust3r),  [Fast3R](https://github.com/facebookresearch/fast3r), [CUT3R](https://github.com/CUT3R/CUT3R), [MV-DUSt3R+](https://github.com/facebookresearch/mvdust3r), [StreamVGGT](https://github.com/wzzheng/StreamVGGT), [VGGT-Long](https://github.com/DengKaiCQ/VGGT-Long), [ToMeSD](https://github.com/dbolya/tomesd) and many other inspiring works in the community.

- Special thanks to [Jianyuan Wang](https://jytime.github.io/) for his valuable discussions and suggestions on this work.

<!-- ## ✍️ Checklist

- [ ] Release the evaluation code on 7 Scenes / NRGBD -->


## ⚖️ License
See the [LICENSE](./LICENSE.txt) file for details about the license under which this code is made available.

## Citation

If you find this project helpful, please consider citing the following paper:
```
@article{shen2025fastvggt,
  title={FastVGGT: Training-Free Acceleration of Visual Geometry Transformer},
  author={Shen, You and Zhang, Zhipeng and Qu, Yansong and Cao, Liujuan},
  journal={arXiv preprint arXiv:2509.02560},
  year={2025}
}
```