base on Depth Pro: Sharp Monocular Metric Depth in Less Than a Second. ## 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*.  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 @inproceedings{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}, booktitle = {International Conference on Learning Representations}, year = {2025}, 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. ", Assign "at most 3 tags" to the expected json: {"id":"12694","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"