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https://github.com/isl-org/MiDaS
Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
https://github.com/isl-org/MiDaS
deeplearning monocular-depth-estimation single-image-depth-prediction
Last synced: 24 days ago
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Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
- Host: GitHub
- URL: https://github.com/isl-org/MiDaS
- Owner: isl-org
- License: mit
- Created: 2019-06-24T14:08:35.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-08-23T10:21:06.000Z (4 months ago)
- Last Synced: 2024-10-29T15:38:04.957Z (about 2 months ago)
- Topics: deeplearning, monocular-depth-estimation, single-image-depth-prediction
- Language: Python
- Homepage:
- Size: 3.31 MB
- Stars: 4,478
- Watchers: 73
- Forks: 620
- Open Issues: 153
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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README
## Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
This repository contains code to compute depth from a single image. It accompanies our [paper](https://arxiv.org/abs/1907.01341v3):
>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun
and our [preprint](https://arxiv.org/abs/2103.13413):
> Vision Transformers for Dense Prediction
> René Ranftl, Alexey Bochkovskiy, Vladlen Koltun
For the latest release MiDaS 3.1, a [technical report](https://arxiv.org/pdf/2307.14460.pdf) and [video](https://www.youtube.com/watch?v=UjaeNNFf9sE&t=3s) are available.
MiDaS was trained on up to 12 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS, KITTI, NYU Depth V2) with
multi-objective optimization.
The original model that was trained on 5 datasets (`MIX 5` in the paper) can be found [here](https://github.com/isl-org/MiDaS/releases/tag/v2).
The figure below shows an overview of the different MiDaS models; the bubble size scales with number of parameters.
### Setup
1) Pick one or more models and download the corresponding weights to the `weights` folder:
MiDaS 3.1
- For highest quality: [dpt_beit_large_512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt)
- For moderately less quality, but better speed-performance trade-off: [dpt_swin2_large_384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt)
- For embedded devices: [dpt_swin2_tiny_256](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt), [dpt_levit_224](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_levit_224.pt)
- For inference on Intel CPUs, OpenVINO may be used for the small legacy model: openvino_midas_v21_small [.xml](https://github.com/isl-org/MiDaS/releases/download/v3_1/openvino_midas_v21_small_256.xml), [.bin](https://github.com/isl-org/MiDaS/releases/download/v3_1/openvino_midas_v21_small_256.bin)
MiDaS 3.0: Legacy transformer models [dpt_large_384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large_384.pt) and [dpt_hybrid_384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid_384.pt)
MiDaS 2.1: Legacy convolutional models [midas_v21_384](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_384.pt) and [midas_v21_small_256](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_small_256.pt)
1) Set up dependencies:
```shell
conda env create -f environment.yaml
conda activate midas-py310
```
#### optional
For the Next-ViT model, execute
```shell
git submodule add https://github.com/isl-org/Next-ViT midas/external/next_vit
```
For the OpenVINO model, install
```shell
pip install openvino
```
### Usage
1) Place one or more input images in the folder `input`.
2) Run the model with
```shell
python run.py --model_type --input_path input --output_path output
```
where `````` is chosen from [dpt_beit_large_512](#model_type), [dpt_beit_large_384](#model_type),
[dpt_beit_base_384](#model_type), [dpt_swin2_large_384](#model_type), [dpt_swin2_base_384](#model_type),
[dpt_swin2_tiny_256](#model_type), [dpt_swin_large_384](#model_type), [dpt_next_vit_large_384](#model_type),
[dpt_levit_224](#model_type), [dpt_large_384](#model_type), [dpt_hybrid_384](#model_type),
[midas_v21_384](#model_type), [midas_v21_small_256](#model_type), [openvino_midas_v21_small_256](#model_type).
3) The resulting depth maps are written to the `output` folder.
#### optional
1) By default, the inference resizes the height of input images to the size of a model to fit into the encoder. This
size is given by the numbers in the model names of the [accuracy table](#accuracy). Some models do not only support a single
inference height but a range of different heights. Feel free to explore different heights by appending the extra
command line argument `--height`. Unsupported height values will throw an error. Note that using this argument may
decrease the model accuracy.
2) By default, the inference keeps the aspect ratio of input images when feeding them into the encoder if this is
supported by a model (all models except for Swin, Swin2, LeViT). In order to resize to a square resolution,
disregarding the aspect ratio while preserving the height, use the command line argument `--square`.
#### via Camera
If you want the input images to be grabbed from the camera and shown in a window, leave the input and output paths
away and choose a model type as shown above:
```shell
python run.py --model_type --side
```
The argument `--side` is optional and causes both the input RGB image and the output depth map to be shown
side-by-side for comparison.
#### via Docker
1) Make sure you have installed Docker and the
[NVIDIA Docker runtime](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-\(Native-GPU-Support\)).
2) Build the Docker image:
```shell
docker build -t midas .
```
3) Run inference:
```shell
docker run --rm --gpus all -v $PWD/input:/opt/MiDaS/input -v $PWD/output:/opt/MiDaS/output -v $PWD/weights:/opt/MiDaS/weights midas
```
This command passes through all of your NVIDIA GPUs to the container, mounts the
`input` and `output` directories and then runs the inference.
#### via PyTorch Hub
The pretrained model is also available on [PyTorch Hub](https://pytorch.org/hub/intelisl_midas_v2/)
#### via TensorFlow or ONNX
See [README](https://github.com/isl-org/MiDaS/tree/master/tf) in the `tf` subdirectory.
Currently only supports MiDaS v2.1.
#### via Mobile (iOS / Android)
See [README](https://github.com/isl-org/MiDaS/tree/master/mobile) in the `mobile` subdirectory.
#### via ROS1 (Robot Operating System)
See [README](https://github.com/isl-org/MiDaS/tree/master/ros) in the `ros` subdirectory.
Currently only supports MiDaS v2.1. DPT-based models to be added.
### Accuracy
We provide a **zero-shot error** $\epsilon_d$ which is evaluated for 6 different datasets
(see [paper](https://arxiv.org/abs/1907.01341v3)). **Lower error values are better**.
$\color{green}{\textsf{Overall model quality is represented by the improvement}}$ ([Imp.](#improvement)) with respect to
MiDaS 3.0 DPTL-384. The models are grouped by the height used for inference, whereas the square training resolution is given by
the numbers in the model names. The table also shows the **number of parameters** (in millions) and the
**frames per second** for inference at the training resolution (for GPU RTX 3090):
| MiDaS Model | DIW WHDR | Eth3d AbsRel | Sintel AbsRel | TUM δ1 | KITTI δ1 | NYUv2 δ1 | $\color{green}{\textsf{Imp.}}$ % | Par.M | FPS |
|-----------------------------------------------------------------------------------------------------------------------|-------------------------:|-----------------------------:|------------------------------:|-------------------------:|-------------------------:|-------------------------:|-------------------------------------------------:|----------------------:|--------------------------:|
| **Inference height 512** | | | | | | | | | |
| [v3.1 BEiTL-512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt) | 0.1137 | 0.0659 | 0.2366 | **6.13** | 11.56* | **1.86*** | $\color{green}{\textsf{19}}$ | **345** | **5.7** |
| [v3.1 BEiTL-512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt)$\tiny{\square}$ | **0.1121** | **0.0614** | **0.2090** | 6.46 | **5.00*** | 1.90* | $\color{green}{\textsf{34}}$ | **345** | **5.7** |
| | | | | | | | | | |
| **Inference height 384** | | | | | | | | | |
| [v3.1 BEiTL-512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt) | 0.1245 | 0.0681 | **0.2176** | **6.13** | 6.28* | **2.16*** | $\color{green}{\textsf{28}}$ | 345 | 12 |
| [v3.1 Swin2L-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt)$\tiny{\square}$ | 0.1106 | 0.0732 | 0.2442 | 8.87 | **5.84*** | 2.92* | $\color{green}{\textsf{22}}$ | 213 | 41 |
| [v3.1 Swin2B-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_base_384.pt)$\tiny{\square}$ | 0.1095 | 0.0790 | 0.2404 | 8.93 | 5.97* | 3.28* | $\color{green}{\textsf{22}}$ | 102 | 39 |
| [v3.1 SwinL-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin_large_384.pt)$\tiny{\square}$ | 0.1126 | 0.0853 | 0.2428 | 8.74 | 6.60* | 3.34* | $\color{green}{\textsf{17}}$ | 213 | 49 |
| [v3.1 BEiTL-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_384.pt) | 0.1239 | **0.0667** | 0.2545 | 7.17 | 9.84* | 2.21* | $\color{green}{\textsf{17}}$ | 344 | 13 |
| [v3.1 Next-ViTL-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_next_vit_large_384.pt) | **0.1031** | 0.0954 | 0.2295 | 9.21 | 6.89* | 3.47* | $\color{green}{\textsf{16}}$ | **72** | 30 |
| [v3.1 BEiTB-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_base_384.pt) | 0.1159 | 0.0967 | 0.2901 | 9.88 | 26.60* | 3.91* | $\color{green}{\textsf{-31}}$ | 112 | 31 |
| [v3.0 DPTL-384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large_384.pt) | 0.1082 | 0.0888 | 0.2697 | 9.97 | 8.46 | 8.32 | $\color{green}{\textsf{0}}$ | 344 | **61** |
| [v3.0 DPTH-384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid_384.pt) | 0.1106 | 0.0934 | 0.2741 | 10.89 | 11.56 | 8.69 | $\color{green}{\textsf{-10}}$ | 123 | 50 |
| [v2.1 Large384](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_384.pt) | 0.1295 | 0.1155 | 0.3285 | 12.51 | 16.08 | 8.71 | $\color{green}{\textsf{-32}}$ | 105 | 47 |
| | | | | | | | | | |
| **Inference height 256** | | | | | | | | | |
| [v3.1 Swin2T-256](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt)$\tiny{\square}$ | **0.1211** | **0.1106** | **0.2868** | **13.43** | **10.13*** | **5.55*** | $\color{green}{\textsf{-11}}$ | 42 | 64 |
| [v2.1 Small256](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_small_256.pt) | 0.1344 | 0.1344 | 0.3370 | 14.53 | 29.27 | 13.43 | $\color{green}{\textsf{-76}}$ | **21** | **90** |
| | | | | | | | | | |
| **Inference height 224** | | | | | | | | | |
| [v3.1 LeViT224](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_levit_224.pt)$\tiny{\square}$ | **0.1314** | **0.1206** | **0.3148** | **18.21** | **15.27*** | **8.64*** | $\color{green}{\textsf{-40}}$ | **51** | **73** |
* No zero-shot error, because models are also trained on KITTI and NYU Depth V2\
$\square$ Validation performed at **square resolution**, either because the transformer encoder backbone of a model
does not support non-square resolutions (Swin, Swin2, LeViT) or for comparison with these models. All other
validations keep the aspect ratio. A difference in resolution limits the comparability of the zero-shot error and the
improvement, because these quantities are averages over the pixels of an image and do not take into account the
advantage of more details due to a higher resolution.\
Best values per column and same validation height in bold
#### Improvement
The improvement in the above table is defined as the relative zero-shot error with respect to MiDaS v3.0
DPTL-384 and averaging over the datasets. So, if $\epsilon_d$ is the zero-shot error for dataset $d$, then
the $\color{green}{\textsf{improvement}}$ is given by $100(1-(1/6)\sum_d\epsilon_d/\epsilon_{d,\rm{DPT_{L-384}}})$%.
Note that the improvements of 10% for MiDaS v2.0 → v2.1 and 21% for MiDaS v2.1 → v3.0 are not visible from the
improvement column (Imp.) in the table but would require an evaluation with respect to MiDaS v2.1 Large384
and v2.0 Large384 respectively instead of v3.0 DPTL-384.
### Depth map comparison
Zoom in for better visibility
### Speed on Camera Feed
Test configuration
- Windows 10
- 11th Gen Intel Core i7-1185G7 3.00GHz
- 16GB RAM
- Camera resolution 640x480
- openvino_midas_v21_small_256
Speed: 22 FPS
### Applications
MiDaS is used in the following other projects from Intel Labs:
- [ZoeDepth](https://arxiv.org/pdf/2302.12288.pdf) (code available [here](https://github.com/isl-org/ZoeDepth)): MiDaS computes the relative depth map given an image. For metric depth estimation, ZoeDepth can be used, which combines MiDaS with a metric depth binning module appended to the decoder.
- [LDM3D](https://arxiv.org/pdf/2305.10853.pdf) (Hugging Face model available [here](https://huggingface.co/Intel/ldm3d-4c)): LDM3D is an extension of vanilla stable diffusion designed to generate joint image and depth data from a text prompt. The depth maps used for supervision when training LDM3D have been computed using MiDaS.
### Changelog
* [Dec 2022] Released [MiDaS v3.1](https://arxiv.org/pdf/2307.14460.pdf):
- New models based on 5 different types of transformers ([BEiT](https://arxiv.org/pdf/2106.08254.pdf), [Swin2](https://arxiv.org/pdf/2111.09883.pdf), [Swin](https://arxiv.org/pdf/2103.14030.pdf), [Next-ViT](https://arxiv.org/pdf/2207.05501.pdf), [LeViT](https://arxiv.org/pdf/2104.01136.pdf))
- Training datasets extended from 10 to 12, including also KITTI and NYU Depth V2 using [BTS](https://github.com/cleinc/bts) split
- Best model, BEiTLarge 512, with resolution 512x512, is on average about [28% more accurate](#Accuracy) than MiDaS v3.0
- Integrated live depth estimation from camera feed
* [Sep 2021] Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/DPT-Large).
* [Apr 2021] Released MiDaS v3.0:
- New models based on [Dense Prediction Transformers](https://arxiv.org/abs/2103.13413) are on average [21% more accurate](#Accuracy) than MiDaS v2.1
- Additional models can be found [here](https://github.com/isl-org/DPT)
* [Nov 2020] Released MiDaS v2.1:
- New model that was trained on 10 datasets and is on average about [10% more accurate](#Accuracy) than [MiDaS v2.0](https://github.com/isl-org/MiDaS/releases/tag/v2)
- New light-weight model that achieves [real-time performance](https://github.com/isl-org/MiDaS/tree/master/mobile) on mobile platforms.
- Sample applications for [iOS](https://github.com/isl-org/MiDaS/tree/master/mobile/ios) and [Android](https://github.com/isl-org/MiDaS/tree/master/mobile/android)
- [ROS package](https://github.com/isl-org/MiDaS/tree/master/ros) for easy deployment on robots
* [Jul 2020] Added TensorFlow and ONNX code. Added [online demo](http://35.202.76.57/).
* [Dec 2019] Released new version of MiDaS - the new model is significantly more accurate and robust
* [Jul 2019] Initial release of MiDaS ([Link](https://github.com/isl-org/MiDaS/releases/tag/v1))
### Citation
Please cite our paper if you use this code or any of the models:
```
@ARTICLE {Ranftl2022,
author = "Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun",
title = "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
year = "2022",
volume = "44",
number = "3"
}
```
If you use a DPT-based model, please also cite:
```
@article{Ranftl2021,
author = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
title = {Vision Transformers for Dense Prediction},
journal = {ICCV},
year = {2021},
}
```
Please cite the technical report for MiDaS 3.1 models:
```
@article{birkl2023midas,
title={MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation},
author={Reiner Birkl and Diana Wofk and Matthias M{\"u}ller},
journal={arXiv preprint arXiv:2307.14460},
year={2023}
}
```
For ZoeDepth, please use
```
@article{bhat2023zoedepth,
title={Zoedepth: Zero-shot transfer by combining relative and metric depth},
author={Bhat, Shariq Farooq and Birkl, Reiner and Wofk, Diana and Wonka, Peter and M{\"u}ller, Matthias},
journal={arXiv preprint arXiv:2302.12288},
year={2023}
}
```
and for LDM3D
```
@article{stan2023ldm3d,
title={LDM3D: Latent Diffusion Model for 3D},
author={Stan, Gabriela Ben Melech and Wofk, Diana and Fox, Scottie and Redden, Alex and Saxton, Will and Yu, Jean and Aflalo, Estelle and Tseng, Shao-Yen and Nonato, Fabio and Muller, Matthias and others},
journal={arXiv preprint arXiv:2305.10853},
year={2023}
}
```
### Acknowledgements
Our work builds on and uses code from [timm](https://github.com/rwightman/pytorch-image-models) and [Next-ViT](https://github.com/bytedance/Next-ViT).
We'd like to thank the authors for making these libraries available.
### License
MIT License