https://github.com/facebookresearch/vggt
[CVPR 2025 Oral] VGGT: Visual Geometry Grounded Transformer
https://github.com/facebookresearch/vggt
Last synced: 6 months ago
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[CVPR 2025 Oral] VGGT: Visual Geometry Grounded Transformer
- Host: GitHub
- URL: https://github.com/facebookresearch/vggt
- Owner: facebookresearch
- License: other
- Created: 2025-02-18T18:32:14.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-04-10T23:37:47.000Z (6 months ago)
- Last Synced: 2025-04-11T00:27:40.097Z (6 months ago)
- Language: Python
- Homepage:
- Size: 63 MB
- Stars: 4,563
- Watchers: 195
- Forks: 362
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
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README
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
- [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
#### 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.