DeFlow: Self-supervised 3D Motion Estimation of Debris Flow ⛰️

Liyuan Zhu, Yuru Jia, Shengyu Huang, Nicholas Meyer, Andreas Wieser, Konrad Schindler, Jordan Aaron

ETH Zurich

Paper | Website | Poster | Dataset

This repository is the official implementation:

DeFlow: Self-supervised 3D Motion Estimation of Debris Flow. CVPRW 2023

Installation 🏞️

First clone our repository:

git clone https://github.com/Zhu-Liyuan/DeFlow
cd DeFlow

You will need to install conda to build the environment.

conda create -n DeFlow python=3.9
conda activate DeFlow
pip install -r requirements.txt

Dataset and pretrained model

We provide preprocessed debris flow dataset. The preprocessed dataset and checkpoint can be downloaded by running:

wget --no-check-certificate --show-progress https://share.phys.ethz.ch/~gsg/DeFlow/DeFlow_Dataset.zip
unzip DeFlow_Dataset.zip
wget --no-check-certificate --show-progress https://share.phys.ethz.ch/~gsg/DeFlow/checkpoint.zip
unzip checkpoint.zip

You can also build your own dataset following the structure below

├── Data
    ├── Cam1
        ├── 000001.jpg
        ├── 000002.jpg
        .
        .
        ├── 00000X.jpg
    ├── Cam2
        ├── 000001.jpg
        ├── 000002.jpg
        .
        .
        ├── 00000X.jpg
    ├── LiDAR
        ├── 000001.ply
        ├── 000002.ply
        .
        .
        ├── 00000X.ply
├── Transformations
        ├── cam_intrinxics.txt
        ├── LiCam_tranformations.txt

To train a model, run:

python main.py --config_path configs/deflow_default.yaml

and you can change the mode to eval in the config file for evaluation.

Contact

If you have any questions, please let me know:

• Liyuan Zhu {[email protected]}

Citation

If you use DeFlow for any academic work, please cite our original paper.

@InProceedings{zhu2023DeFlow,
author = {Liyuan Zhu and Yuru Jia and Shengyu Huang and Nicholas Meyer and Andreas Wieser and Konrad Schindler, Jordan Aaron},
title = {DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2023}
}

Additionally, we thank the respective developers of the following open-source projects:

• PCAccumulation
• CamLiFlow
• Self-mono-sf