base on Universal Monocular Metric Depth Estimation [](https://arxiv.org/abs/2403.18913) [](https://lpiccinelli-eth.github.io/pub/unidepth/) <!-- [](https://huggingface.co/spaces/lpiccinelli/UniDepth) --> [-orange)](https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction) [](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=unidepth-universal-monocular-metric-depth) [](https://paperswithcode.com/sota/monocular-depth-estimation-on-kitti-eigen?p=unidepth-universal-monocular-metric-depth) # UniDepth: Universal Monocular Metric Depth Estimation  > [**UniDepth: Universal Monocular Metric Depth Estimation**](https://arxiv.org/abs/2403.18913), > Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, Fisher Yu, > CVPR 2024, > *Paper at [arXiv 2403.18913](https://arxiv.org/pdf/2403.18913.pdf)* ## News and ToDo - [ ] Release UniDepth on PyPI. - [ ] Release HuggingFace/Gradio demo. - [ ] Solve image corners artifacts (retraining in progress...) - [x] `12.06.2024`: Release smaller V2 models. - [x] `01.05.2024`: Release UniDepthV2. - [x] `02.04.2024`: Release UniDepth as python package. - [x] `01.04.2024`: Inference code and V1 models are released. - [x] `26.02.2024`: UniDepth is accepted at CVPR 2024! (Highlight :star:) ## Zero-Shot Visualization ### YouTube (The Office - Parkour) <p align="center"> <img src="assets/docs/theoffice.gif" alt="animated" /> </p> ### NuScenes (stitched cameras) <p align="center"> <img src="assets/docs/nuscenes_surround.gif" alt="animated" /> </p> ## Installation Requirements are not in principle hard requirements, but there might be some differences (not tested): - Linux - Python 3.10+ - CUDA 11.8 Install the environment needed to run UniDepth with: ```shell export VENV_DIR=<YOUR-VENVS-DIR> export NAME=Unidepth python -m venv $VENV_DIR/$NAME source $VENV_DIR/$NAME/bin/activate # Install UniDepth and dependencies pip install -e . --extra-index-url https://download.pytorch.org/whl/cu118 # Install Pillow-SIMD (Optional) pip uninstall pillow CC="cc -mavx2" pip install -U --force-reinstall pillow-simd ``` If you use conda, you should change the following: ```shell python -m venv $VENV_DIR/$NAME -> conda create -n $NAME python=3.11 source $VENV_DIR/$NAME/bin/activate -> conda activate $NAME ``` *Note*: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/). *Note*: xFormers may raise the the Runtime "error": `Triton Error [CUDA]: device kernel image is invalid`. This is related to xFormers mismatching system-wide CUDA and CUDA shipped with torch. It may considerably slow down inference. Run UniDepth on the given assets to test your installation (you can check this script as guideline for further usage): ```shell python ./scripts/demo.py ``` If everything runs correctly, `demo.py` should print: `ARel: 5.13%`. If you encounter `Segmentation Fault` after running the demo, you may need to uninstall torch via pip (`pip uninstall torch`) and install the torch version present in [requirements](requirements.txt) with `conda`. ## Get Started After installing the dependencies, you can load the pre-trained models easily from [Hugging Face](https://huggingface.co/models?other=UniDepth) as follows: ```python from unidepth.models import UniDepthV1 model = UniDepthV1.from_pretrained("lpiccinelli/unidepth-v1-vitl14") # or "lpiccinelli/unidepth-v1-cnvnxtl" for the ConvNext backbone ``` Then you can generate the metric depth estimation and intrinsics prediction directly from RGB image only as follows: ```python import numpy as np from PIL import Image # Move to CUDA, if any device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) # Load the RGB image and the normalization will be taken care of by the model rgb = torch.from_numpy(np.array(Image.open(image_path))).permute(2, 0, 1) # C, H, W predictions = model.infer(rgb) # Metric Depth Estimation depth = predictions["depth"] # Point Cloud in Camera Coordinate xyz = predictions["points"] # Intrinsics Prediction intrinsics = predictions["intrinsics"] ``` You can use ground truth intrinsics as input to the model as well: ```python intrinsics_path = "assets/demo/intrinsics.npy" # Load the intrinsics if available intrinsics = torch.from_numpy(np.load(intrinsics_path)) # 3 x 3 predictions = model.infer(rgb, intrinsics) ``` To use the forward method for your custom training, you should: 1) Take care of the dataloading: a) ImageNet-normalization b) Long-edge based resizing (and padding) with input shape provided in `image_shape` under configs c) `BxCxHxW` format d) If any intriniscs given, adapt them accordingly to your resizing 2) Format the input data structure as: ```python data = {"image": rgb, "K": intrinsics} predictions = model(data, {}) ``` ## Model Zoo The available models are the following: <table border="0"> <tr> <th>Model</th> <th>Backbone</th> <th>Name</th> </tr> <tr> <td rowspan="2"><b>UnidepthV1</b></td> <td>ConvNext-L</td> <td><a href="https://huggingface.co/lpiccinelli/unidepth-v1-cnvnxtl">unidepth-v1-cnvnxtl</a></td> </tr> <tr> <td>ViT-L</td> <td><a href="https://huggingface.co/lpiccinelli/unidepth-v1-vitl14">unidepth-v1-vitl14</a></td> </tr> <hr style="border: 2px solid black;"> <tr> <td rowspan="3"><b>UnidepthV2</b></td> <td>ViT-S</td> <td><a href="https://huggingface.co/lpiccinelli/unidepth-v2-vits14">unidepth-v2-vits14</a></td> </tr> <tr> <td>ViT-B</td> <td>unidepth-v1-vitb14 (Coming Soon)</td> </tr> <tr> <td>ViT-L</td> <td><a href="https://huggingface.co/lpiccinelli/unidepth-v2-vitl14">unidepth-v2-vitl14</a></td> </tr> </table> Please visit [Hugging Face](https://huggingface.co/lpiccinelli) or click on the links above to access the repo models with weights. You can load UniDepth as the following, with `name` variable matching the table above: ```python from unidepth.models import UniDepthV1, UniDepthV2 model_v1 = UniDepthV1.from_pretrained(f"lpiccinelli/{name}") model_v2 = UniDepthV2.from_pretrained(f"lpiccinelli/{name}") ``` In addition, we provide loading from TorchHub as: ```python version = "v2" backbone = "vitl14" model = torch.hub.load("lpiccinelli-eth/UniDepth", "UniDepth", version=version, backbone=backbone, pretrained=True, trust_repo=True, force_reload=True) ``` You can look into function `UniDepth` in [hubconf.py](hubconf.py) to see how to instantiate the model from local file: provide a local `path` in line 34. ## UniDepthV2 Visit [UniDepthV2 ReadMe](assets/docs/V2_README.md) for a more detailed changelog. To summarize the main differences are: - Input shape and ratio flexibility. - Confidence output - Decoder design - Faster inference - ONNX support ## Results ### Metric Depth Estimation The performance reported is for UniDepthV1 model and the metrics is d1 (higher is better) on zero-shot evaluation. The common split between SUN-RGBD and NYUv2 is removed from SUN-RGBD validation set for evaluation. *: non zero-shot on NYUv2 and KITTI. | Model | NYUv2 | SUN-RGBD | ETH3D | Diode (In) | IBims-1 | KITTI | Nuscenes | DDAD | | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | BTS* | 88.5 | 76.1 | 26.8 | 19.2 | 53.1 | 96.2 | 33.7 | 43.0 | | AdaBins* | 90.1 | 77.7 | 24.3 | 17.4 | 55.0 | 96.3 | 33.3 | 37.7 | | NeWCRF* | 92.1 | 75.3 | 35.7 | 20.1 | 53.6 | 97.5 | 44.2 | 45.6 | | iDisc* | 93.8 | 83.7 | 35.6 | 23.8 | 48.9 | 97.5 | 39.4 | 28.4 | | ZoeDepth* | 95.2 | 86.7 | 35.0 | 36.9 | 58.0 | 96.5 | 28.3 | 27.2 | | Metric3D | 92.6 | 15.4 | 45.6 | 39.2 | 79.7 | 97.5 | 72.3 | - | | UniDepth_ConvNext | 97.2| 94.8 | 49.8 | 60.2 | 79.7 | 97.2 | 83.3 | 83.2 | | UniDepth_ViT | 98.4 | 96.6 | 32.6 | 77.1 | 23.9 | 98.6 | 86.2 | 86.4 | ## Contributions If you find any bug in the code, please report to Luigi Piccinelli ([email protected]) ## Citation If you find our work useful in your research please consider citing our publication: ```bibtex @inproceedings{piccinelli2024unidepth, title = {{U}ni{D}epth: Universal Monocular Metric Depth Estimation}, author = {Piccinelli, Luigi and Yang, Yung-Hsu and Sakaridis, Christos and Segu, Mattia and Li, Siyuan and Van Gool, Luc and Yu, Fisher}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2024} } ``` ## License This software is released under Creatives Common BY-NC 4.0 license. You can view a license summary [here](LICENSE). ## Acknowledgement We would like to express our gratitude to [@niels](https://huggingface.co/nielsr) for helping intergrating UniDepth in HuggingFace. This work is funded by Toyota Motor Europe via the research project [TRACE-Zurich](https://trace.ethz.ch) (Toyota Research on Automated Cars Europe). 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