⚡️ FastVGGT: Training-Free Acceleration of Visual Geometry Transformer

Media Analytics & Computing Laboratory; AUTOLAB

You Shen, Zhipeng Zhang, Yansong Qu, Liujuan Cao

• [Sep 3, 2025] Paper release.
• [Sep 2, 2025] Code release.

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.

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

conda create -n fastvggt python=3.10
conda activate fastvggt
git clone [email protected]: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):

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:

    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",
    )

Note: A large number of input_frames may significantly slow down saving the visualization results. Please try using a smaller number first.

python eval_scannet.py --input_frame 30 --vis_attn_map

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

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:

python eval_scannet.py --input_frame 1000 --merging 0

Evaluate Baseline VGGT on the ScanNet dataset with 1,000 input images:

python eval_scannet.py --input_frame 1000

• Thanks to these great repositories: VGGT, Dust3r, Fast3R, CUT3R, MV-DUSt3R+, StreamVGGT, VGGT-Long and many other inspiring works in the community.

• Special thanks to Jianyuan Wang for his valuable discussions and suggestions on this work.

• Release the evaluation code on 7 Scenes / NRGBD

The FastVGGT codebase follows VGGT's license, please refer to LICENSE for applicable terms.

Please note that only this model checkpoint allows commercial usage.