On the Benefit of Adversarial Learning for Monocular Depth Estimation

This is the repository for our CVIU work On the Benefit of Adversarial Learning for Monocular Depth Estimation.
arXiv
CVIU

Two works have served as baselines for this work:
Unsupervised Monocular Depth Estimation with Left-Right Consistency
arXiv

Unsupervised Adversarial Depth Estimation using Cycled Generative Networks arXiv

This repository implements the basic training and evaluation code, to prevent clutter.

Dependencies

A requirements file is available to retrieve all dependencies. Create a new python environment and install using:

pip install -r requirements.txt

Training

Models can be trained by specifying your data directory, a model name and any architecture.

python main.py --data_dir data/ --model_name [MODEL_NAME] --architecture wgan

Resume training is possible by filling in the resume flag with the path to the saved model:

python main.py --data_dir data/ --model_name [MODEL_NAME] --architecture wgan --resume saved_models/[MODEL_NAME]/model_best.pth.tar

There are many, many options for training the models. Have a look at the options with three python files containing options for training, testing and evaluation.

Testing

To test change the --mode flag to test, the network will output the disparities in the output folder.

python main.py --data_dir data/ --model_name [MODEL_NAME] --mode test

Evaluation of Depth

Run the following script to run any evaluation, given that a disparities file is present in output:

python evaluate.py --data_dir data/ --predicted_disp_path output/disparities_[DATASET]_[MODEL_NAME].npy  

Data

This work has been trained on rectified stereo pairs. For this two datasets have been used: KITTI and CityScapes.

KITTI

In this work the split of eigen is used to train and test model. This set contains 22600 training images, 888 validation imagesn and 697 test images.
In the filenames folder there are lists that detail which images correspond to which set. All data can be downloaded by running:

wget -i utils/kitti_archives_to_download.txt -P ~/my/output/folder/

CityScapes

To access data of the CityScapes dataset, one has to register an account and then request special access to the ground truth disparities.
When this data is retrieved the following directories should be put in the data folder:
cs_camera/ with all camera parameters.
cs_disparity/ with all ground truth disparities.
cs_leftImg8bit/ with all left images.
cs_rightImg8bit/ with all right images.

Results

Results are available upon request.

References

A few repositories were the inspiration for this work. These are:

Unsupervised Monocular Depth Estimation with Left-Right Consistency
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
Club AI's Pytorch Implementation of MonoDepth
Cycle GAN and Pix2Pix in Pytorch

Citation

If this work was useful for your research, please consider citing:

@article{groenendijk2020benefit,
  title={On the benefit of adversarial training for monocular depth estimation},
  author={Groenendijk, Rick and Karaoglu, Sezer and Gevers, Theo and Mensink, Thomas},
  journal={Computer Vision and Image Understanding},
  volume={190},
  pages={102848},
  year={2020},
  publisher={Elsevier}
}