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[ICCV 2019] Monocular depth estimation from a single image
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# Monodepth2
This is the reference PyTorch implementation for training and testing depth estimation models using the method described in
> **Digging into Self-Supervised Monocular Depth Prediction**
>
> [Clément Godard](http://www0.cs.ucl.ac.uk/staff/C.Godard/), [Oisin Mac Aodha](http://vision.caltech.edu/~macaodha/), [Michael Firman](http://www.michaelfirman.co.uk) and [Gabriel J. Brostow](http://www0.cs.ucl.ac.uk/staff/g.brostow/)
>
> [ICCV 2019 (arXiv pdf)](https://arxiv.org/abs/1806.01260)
<p align="center">
<img src="assets/teaser.gif" alt="example input output gif" width="600" />
</p>
This code is for non-commercial use; please see the [license file](LICENSE) for terms.
If you find our work useful in your research please consider citing our paper:
```
@article{monodepth2,
title = {Digging into Self-Supervised Monocular Depth Prediction},
author = {Cl{\'{e}}ment Godard and
Oisin {Mac Aodha} and
Michael Firman and
Gabriel J. Brostow},
booktitle = {The International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```
## ⚙️ Setup
Assuming a fresh [Anaconda](https://www.anaconda.com/download/) distribution, you can install the dependencies with:
```shell
conda install pytorch=0.4.1 torchvision=0.2.1 -c pytorch
pip install tensorboardX==1.4
conda install opencv=3.3.1 # just needed for evaluation
```
We ran our experiments with PyTorch 0.4.1, CUDA 9.1, Python 3.6.6 and Ubuntu 18.04.
We have also successfully trained models with PyTorch 1.0, and our code is compatible with Python 2.7. You may have issues installing OpenCV version 3.3.1 if you use Python 3.7, we recommend to create a virtual environment with Python 3.6.6 `conda create -n monodepth2 python=3.6.6 anaconda `.
<!-- We recommend using a [conda environment](https://conda.io/docs/user-guide/tasks/manage-environments.html) to avoid dependency conflicts.
We also recommend using `pillow-simd` instead of `pillow` for faster image preprocessing in the dataloaders. -->
## 🖼️ Prediction for a single image
You can predict scaled disparity for a single image with:
```shell
python test_simple.py --image_path assets/test_image.jpg --model_name mono+stereo_640x192
```
or, if you are using a stereo-trained model, you can estimate metric depth with
```shell
python test_simple.py --image_path assets/test_image.jpg --model_name mono+stereo_640x192 --pred_metric_depth
```
On its first run either of these commands will download the `mono+stereo_640x192` pretrained model (99MB) into the `models/` folder.
We provide the following options for `--model_name`:
| `--model_name` | Training modality | Imagenet pretrained? | Model resolution | KITTI abs. rel. error | delta < 1.25 |
|-------------------------|-------------------|--------------------------|-----------------|------|----------------|
| [`mono_640x192`](https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono_640x192.zip) | Mono | Yes | 640 x 192 | 0.115 | 0.877 |
| [`stereo_640x192`](https://storage.googleapis.com/niantic-lon-static/research/monodepth2/stereo_640x192.zip) | Stereo | Yes | 640 x 192 | 0.109 | 0.864 |
| [`mono+stereo_640x192`](https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono%2Bstereo_640x192.zip) | Mono + Stereo | Yes | 640 x 192 | 0.106 | 0.874 |
| [`mono_1024x320`](https://storage.googleapis.com/niantic-lon-static/research/monodepth2/mono_1024x320.zip) | Mono | Yes | 1024 x 320 | 0.115 | 0.879 |
| [`stereo_1024x320`](https://storage.googleapis.com/niantic-lon-static/research/monodepth2/stereo_1024x320.zip) | Stereo | Yes | 1024 x 320 | 0.107 | 0.874 |
| [`mono+stereo_1024x320`Excerpt of 14,406 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cca982e0f47e7fd6, topic:deep-learning, topic:neural-network, topic:pytorch
matched fp:cca982e0f47e7fd6, topic:computer-vision, readme:computer vision, desc:depth estimation
