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https://github.com/lpiccinelli-eth/unidepth

Universal Monocular Metric Depth Estimation
https://github.com/lpiccinelli-eth/unidepth

3d-reconstruction computer-vision depth-estimation

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Universal Monocular Metric Depth Estimation

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[![arXiv](https://img.shields.io/badge/arXiv-2403.18913-blue?logo=arxiv&color=%23B31B1B)](https://arxiv.org/abs/2403.18913)

[![ProjectPage](https://img.shields.io/badge/Project_Page-UniDepth-blue)](https://lpiccinelli-eth.github.io/pub/unidepth/)



[![KITTI Benchmark](https://img.shields.io/badge/KITTI%20Benchmark-1st%20(at%20submission%20time)-orange)](https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unidepth-universal-monocular-metric-depth/monocular-depth-estimation-on-nyu-depth-v2)](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=unidepth-universal-monocular-metric-depth)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unidepth-universal-monocular-metric-depth/monocular-depth-estimation-on-kitti-eigen)](https://paperswithcode.com/sota/monocular-depth-estimation-on-kitti-eigen?p=unidepth-universal-monocular-metric-depth)



# UniDepth: Universal Monocular Metric Depth Estimation



![](assets/docs/unidepth-banner.png)



> [**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)



  animated




### NuScenes (stitched cameras)



  animated




## 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=

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:



    

        Model

        Backbone

        Name

    

    

        UnidepthV1

        ConvNext-L

        unidepth-v1-cnvnxtl

    

    

        ViT-L

        unidepth-v1-vitl14

    

    


    

        UnidepthV2

        ViT-S

        unidepth-v2-vits14

    

    

        ViT-B

        unidepth-v1-vitb14 (Coming Soon)

    

    

        ViT-L

        unidepth-v2-vitl14

    



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).