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README.md

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+<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}
+}
+```