README.md

---
license: mit
pipeline_tag: depth-estimation
---

## Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

This software project accompanies the research paper:
**[Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073)**, 
*Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, and Vladlen Koltun*.

![](https://cdn-uploads.huggingface.co/production/uploads/6437ed1fa1fa778a4da872a9/zjyBNH8bLuc2V93wOR0Ks.jpeg)

We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image.


The model in this repository is a reference implementation, which has been re-trained. Its performance is close to the model reported in the paper but does not match it exactly.

## Getting Started

We recommend setting up a virtual environment. Using e.g. miniconda, the `depth_pro` package can be installed via:

```bash
conda create -n depth-pro -y python=3.9
conda activate depth-pro

pip install -e .
```

To download pretrained checkpoints follow the code snippet below:
```bash
source get_pretrained_models.sh   # Files will be downloaded to `checkpoints` directory.
```

### Running from commandline

We provide a helper script to directly run the model on a single image:
```bash
# Run prediction on a single image:
depth-pro-run -i ./data/example.jpg
# Run `depth-pro-run -h` for available options.
```

### Running from python

```python
from PIL import Image
import depth_pro

# Load model and preprocessing transform
model, transform = depth_pro.create_model_and_transforms()
model.eval()

# Load and preprocess an image.
image, _, f_px = depth_pro.load_rgb(image_path)
image = transform(image)

# Run inference.
prediction = model.infer(image, f_px=f_px)
depth = prediction["depth"]  # Depth in [m].
focallength_px = prediction["focallength_px"]  # Focal length in pixels.
```


### Evaluation (boundary metrics) 

Our boundary metrics can be found under `eval/boundary_metrics.py` and used as follows:

```python
# for a depth-based dataset
boundary_f1 = SI_boundary_F1(predicted_depth, target_depth)

# for a mask-based dataset (image matting / segmentation) 
boundary_recall = SI_boundary_Recall(predicted_depth, target_mask)
```


## Citation

If you find our work useful, please cite the following paper:

```bibtex
@article{Bochkovskii2024:arxiv,
  author     = {Aleksei Bochkovskii and Ama\"{e}l Delaunoy and Hugo Germain and Marcel Santos and
               Yichao Zhou and Stephan R. Richter and Vladlen Koltun}
  title      = {Depth Pro: Sharp Monocular Metric Depth in Less Than a Second},
  journal    = {arXiv},
  year       = {2024},
  url        = {https://arxiv.org/abs/2410.02073},
}
```

## License
This sample code is released under the [LICENSE](LICENSE) terms.

The model weights are released under the [LICENSE](LICENSE) terms.

## Acknowledgements

Our codebase is built using multiple opensource contributions, please see [Acknowledgements](ACKNOWLEDGEMENTS.md) for more details.

Please check the paper for a complete list of references and datasets used in this work.