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Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models

This repository holds all the code and data for the paper:

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models, ICRA 2024.
Madhu Vankadari, Samuel Hodgson, Sangyun Shin, Kaichen Zhou, Andrew Markham,Niki Trigoni

If you find this code useful, please consider citing:

@inProceedings{vankadari2024dtd,
  title={{Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models}},
  author={Madhu Vankadari, Samuel Hodgson, Sangyun Shin, Kaichen Zhou, Andrew Markham, and Niki Trigoni}
  booktitle={ICRA},
  year={2024},
}

if you have any questions, please email madhu.vankadari@cs.ox.ac.uk

Usage Instructions

We use several other repositories, so please use the following command that automatically downloads all the included submodules for easy use:

git clone --recurse-submodules https://github.com/madhubabuv/dtd.git

Datasets

RobotCar Dataset

Please create an account on the official RobotCar dataset page before downloading the dataset.

cd datasets/robotcar/RobotCarDataset-Scraper

python scrape_mrgdatashare.py --username <placeholder> --password <placehodler> --datasets_file ../datasets.csv --downloads_dir <placehodler>

This should create a folder structure like the following

<dataset root>
    |- stereo.timestamps
    |- lms_front.timestamps
    |- stereo
        |- left
            |- timestamp1.png
            |- timestamp2.png
            ...
        |- right
            |- timestamp1.png
            |- timestamp2.png
            ...
    |- lms_front
        |- timestamp1.png
        |- timestamp2.png
        ...
    |- vo
        |- vo.csv
    |- gps
        |- gps.csv
        |- ins.csv

Now the downloaded dataset is in Bayer format and we should convert the images to RGB format. To do that,

cd datasets/robotcar/sdk
python convert_images.py --data_path <placeholder>

Please convert images from the both left and right directories. This process going to take a bit of time (10x of minutes).

Testing

Please download the pretrained checkpoints from here on RobotCarDataset. If you want to test it on the robotcar dataset test split, use the following

python test.py --test_file_path datasets/robotcar/files/2014-12-16-18-44-24_test.txt --checkpoint_path <downloaded checkpoint> --save_dir <whereever you want to save>

Evaluation

We need to download the ground depths that are calculated using RTK data released along with the dataset using the robotcar-SDK from here.

python evaluation/eval_depth_weighted.py --gt_depths_path <ground-truth depth path> --pred_disp_path <prediction path>

If you see a small change in the results from the paper, that is mainly because of the conversion of depths to fp16 while sharing on Google Drive.

Training

We are going to use the splits proposed in When the sun goes down for training. The training files are already there on datasets/robotcar/files. The original file was for monocular training, hence the 3 columns, we are going to use the middle column

python train.py --data_path <place holder> --checkpoint_dir <place holder> --num_epochs 20 --learning_rate 0.0001

License

This repository is released under the MIT Licence license as found in the LICENSE file.

Acknowledgments

This project would not have been possible without the awesome repo Unimatch. We thank the original others for their excellent work