VGGT: Visual Geometry Grounded Transformer

**[Visual Geometry Group, University of Oxford](https://www.robots.ox.ac.uk/~vgg/)**; **[Meta AI](https://ai.facebook.com/research/)** [Jianyuan Wang](https://jytime.github.io/), [Minghao Chen](https://silent-chen.github.io/), [Nikita Karaev](https://nikitakaraevv.github.io/), [Andrea Vedaldi](https://www.robots.ox.ac.uk/~vedaldi/), [Christian Rupprecht](https://chrirupp.github.io/), [David Novotny](https://d-novotny.github.io/)

```bibtex @inproceedings{wang2025vggt, title={VGGT: Visual Geometry Grounded Transformer}, author={Wang, Jianyuan and Chen, Minghao and Karaev, Nikita and Vedaldi, Andrea and Rupprecht, Christian and Novotny, David}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2025} } ``` ## Updates - [May 3, 2025] Evaluation code for reproducing our camera pose estimation results on Co3D is now available in the [evaluation](https://github.com/facebookresearch/vggt/tree/evaluation) branch. VGGT+BA implementation is coming. - [Apr 13, 2025] Training code is being gradually cleaned and uploaded to the [training](https://github.com/facebookresearch/vggt/tree/training) branch. It will be merged into the main branch once finalized. ## Overview Visual Geometry Grounded Transformer (VGGT, CVPR 2025) is a feed-forward neural network that directly infers all key 3D attributes of a scene, including extrinsic and intrinsic camera parameters, point maps, depth maps, and 3D point tracks, **from one, a few, or hundreds of its views, within seconds**. ## Quick Start First, clone this repository to your local machine, and install the dependencies (torch, torchvision, numpy, Pillow, and huggingface_hub). ```bash git clone git@github.com:facebookresearch/vggt.git cd vggt pip install -r requirements.txt ``` Alternatively, you can install VGGT as a package (click here for details). Now, try the model with just a few lines of code: ```python import torch from vggt.models.vggt import VGGT from vggt.utils.load_fn import load_and_preprocess_images device = "cuda" if torch.cuda.is_available() else "cpu" # bfloat16 is supported on Ampere GPUs (Compute Capability 8.0+) dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16 # Initialize the model and load the pretrained weights. # This will automatically download the model weights the first time it's run, which may take a while. model = VGGT.from_pretrained("facebook/VGGT-1B").to(device) # Load and preprocess example images (replace with your own image paths) image_names = ["path/to/imageA.png", "path/to/imageB.png", "path/to/imageC.png"] images = load_and_preprocess_images(image_names).to(device) with torch.no_grad(): with torch.cuda.amp.autocast(dtype=dtype): # Predict attributes including cameras, depth maps, and point maps. predictions = model(images) ``` The model weights will be automatically downloaded from Hugging Face. If you encounter issues such as slow loading, you can manually download them [here](https://huggingface.co/facebook/VGGT-1B/blob/main/model.pt) and load, or: ```python model = VGGT() _URL = "https://huggingface.co/facebook/VGGT-1B/resolve/main/model.pt" model.load_state_dict(torch.hub.load_state_dict_from_url(_URL)) ``` ## Detailed Usage You can also optionally choose which attributes (branches) to predict, as shown below. This achieves the same result as the example above. This example uses a batch size of 1 (processing a single scene), but it naturally works for multiple scenes. ```python from vggt.utils.pose_enc import pose_encoding_to_extri_intri from vggt.utils.geometry import unproject_depth_map_to_point_map with torch.no_grad(): with torch.cuda.amp.autocast(dtype=dtype): images = images[None] # add batch dimension aggregated_tokens_list, ps_idx = model.aggregator(images) # Predict Cameras pose_enc = model.camera_head(aggregated_tokens_list)[-1] # Extrinsic and intrinsic matrices, following OpenCV convention (camera from world) extrinsic, intrinsic = pose_encoding_to_extri_intri(pose_enc, images.shape[-2:]) # Predict Depth Maps depth_map, depth_conf = model.depth_head(aggregated_tokens_list, images, ps_idx) # Predict Point Maps point_map, point_conf = model.point_head(aggregated_tokens_list, images, ps_idx) # Construct 3D Points from Depth Maps and Cameras # which usually leads to more accurate 3D points than point map branch point_map_by_unprojection = unproject_depth_map_to_point_map(depth_map.squeeze(0), extrinsic.squeeze(0), intrinsic.squeeze(0)) # Predict Tracks # choose your own points to track, with shape (N, 2) for one scene query_points = torch.FloatTensor([[100.0, 200.0], [60.72, 259.94]]).to(device) track_list, vis_score, conf_score = model.track_head(aggregated_tokens_list, images, ps_idx, query_points=query_points[None]) ``` Furthermore, if certain pixels in the input frames are unwanted (e.g., reflective surfaces, sky, or water), you can simply mask them by setting the corresponding pixel values to 0 or 1. Precise segmentation masks aren't necessary - simple bounding box masks work effectively (check this [issue](https://github.com/facebookresearch/vggt/issues/47) for an example). ## Visualization We provide multiple ways to visualize your 3D reconstructions and tracking results. Before using these visualization tools, install the required dependencies: ```bash pip install -r requirements_demo.txt ``` ### Interactive 3D Visualization **Please note:** VGGT typically reconstructs a scene in less than 1 second. However, visualizing 3D points may take tens of seconds due to third-party rendering, independent of VGGT's processing time. The visualization is slow especially when the number of images is large. #### Gradio Web Interface Our Gradio-based interface allows you to upload images/videos, run reconstruction, and interactively explore the 3D scene in your browser. You can launch this in your local machine or try it on [Hugging Face](https://huggingface.co/spaces/facebook/vggt). ```bash python demo_gradio.py ```

Click to preview the Gradio interactive interface

![Gradio Web Interface Preview](https://jytime.github.io/data/vggt_hf_demo_screen.png)

#### Viser 3D Viewer Run the following command to run reconstruction and visualize the point clouds in viser. Note this script requires a path to a folder containing images. It assumes only image files under the folder. You can set `--use_point_map` to use the point cloud from the point map branch, instead of the depth-based point cloud. ```bash python demo_viser.py --image_folder path/to/your/images/folder ``` ### Track Visualization To visualize point tracks across multiple images: ```python from vggt.utils.visual_track import visualize_tracks_on_images track = track_list[-1] visualize_tracks_on_images(images, track, (conf_score>0.2) & (vis_score>0.2), out_dir="track_visuals") ``` This plots the tracks on the images and saves them to the specified output directory. ## Single-view Reconstruction Our model shows surprisingly good performance on single-view reconstruction, although it was never trained for this task. The model does not need to duplicate the single-view image to a pair, instead, it can directly infer the 3D structure from the tokens of the single view image. Feel free to try it with our demos above, which naturally works for single-view reconstruction. We did not quantitatively test monocular depth estimation performance ourselves, but [@kabouzeid](https://github.com/kabouzeid) generously provided a comparison of VGGT to recent methods [here](https://github.com/facebookresearch/vggt/issues/36). VGGT shows competitive or better results compared to state-of-the-art monocular approaches such as DepthAnything v2 or MoGe, despite never being explicitly trained for single-view tasks. ## Runtime and GPU Memory We benchmark the runtime and GPU memory usage of VGGT's aggregator on a single NVIDIA H100 GPU across various input sizes. | **Input Frames** | 1 | 2 | 4 | 8 | 10 | 20 | 50 | 100 | 200 | |:----------------:|:-:|:-:|:-:|:-:|:--:|:--:|:--:|:---:|:---:| | **Time (s)** | 0.04 | 0.05 | 0.07 | 0.11 | 0.14 | 0.31 | 1.04 | 3.12 | 8.75 | | **Memory (GB)** | 1.88 | 2.07 | 2.45 | 3.23 | 3.63 | 5.58 | 11.41 | 21.15 | 40.63 | Note that these results were obtained using Flash Attention 3, which is faster than the default Flash Attention 2 implementation while maintaining almost the same memory usage. Feel free to compile Flash Attention 3 from source to get better performance. ## Research Progression Our work builds upon a series of previous research projects. If you're interested in understanding how our research evolved, check out our previous works:

Deep SfM Revisited	──┐
PoseDiffusion	─────►	VGGSfM ──► VGGT
CoTracker	──┘

## Acknowledgements Thanks to these great repositories: [PoseDiffusion](https://github.com/facebookresearch/PoseDiffusion), [VGGSfM](https://github.com/facebookresearch/vggsfm), [CoTracker](https://github.com/facebookresearch/co-tracker), [DINOv2](https://github.com/facebookresearch/dinov2), [Dust3r](https://github.com/naver/dust3r), [Moge](https://github.com/microsoft/moge), [PyTorch3D](https://github.com/facebookresearch/pytorch3d), [Sky Segmentation](https://github.com/xiongzhu666/Sky-Segmentation-and-Post-processing), [Depth Anything V2](https://github.com/DepthAnything/Depth-Anything-V2), [Metric3D](https://github.com/YvanYin/Metric3D) and many other inspiring works in the community. ## Checklist - [ ] Release the training code - [ ] Release VGGT-500M and VGGT-200M ## License See the [LICENSE](./LICENSE.txt) file for details about the license under which this code is made available.