# GCNDepth

**Repository Path**: godycc/GCNDepth

## Basic Information

- **Project Name**: GCNDepth
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-31
- **Last Updated**: 2021-12-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GCNDepth
Self-Supervised CNN-GCN Autoencoder 
> **GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network**
>
> https://arxiv.org/abs/2112.06782

If you find our work useful in your research please consider citing our paper:

```
@misc{masoumian2021gcndepth,
      title={GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network}, 
      author={Armin Masoumian and Hatem A. Rashwan and Saddam Abdulwahab and Julian Cristiano and Domenec Puig},
      year={2021},
      eprint={2112.06782},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## Setup

### Requirements:
- PyTorch1.2+, Python3.5+, Cuda10.0+
- mmcv==0.4.4


```bash



# this create new conda enviroment to run the model
conda create --name gcndepth python=3.7
conda activate gcndepth

# this installs the right pip and dependencies for the fresh python
conda install ipython
conda install pip

# install required packages from requirements.txt
pip install -r requirements.txt
```

## KITTI training data

Our training data is the same with other self-supervised monocular depth estimation methods, please refer to [monodepth2](https://github.com/nianticlabs/monodepth2) to prepare the training data.

## pretrained weights

We provide weights for [GCNDepth](https://drive.google.com/file/d/1BImXNB9PEgv3mZczB3uBW3EDi4dpOcXF/view?usp=sharing)

## API
We provide an API interface for you to predict depth and pose from an image sequence and visulize some results.
They are stored in folder 'scripts'.

```
eval_pose.py is used to obtain kitti odometry evaluation results.
```

```
eval_depth.py is used to obtain kitti depth evaluation results.
```

```
infer.py is used to generate depth maps from given models.
```

## Training
You can use following command to launch distributed learning of our model:
```
python run.py
```