Liyuan Zhu, Yuru Jia, Shengyu Huang, Nicholas Meyer, Andreas Wieser, Konrad Schindler, Jordan Aaron
ETH Zurich
This repository is the official implementation:
DeFlow: Self-supervised 3D Motion Estimation of Debris Flow. CVPRW 2023
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
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
โโโ Data
โโโ Cam1
โโโ 000001.jpg
โโโ 000002.jpg
.
.
โโโ 00000X.jpg
โโโ Cam2
โโโ 000001.jpg
โโโ 000002.jpg
.
.
โโโ 00000X.jpg
โโโ LiDAR
โโโ 000001.ply
โโโ 000002.ply
.
.
โโโ 00000X.ply
โโโ Transformations
โโโ cam_intrinxics.txt
โโโ 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.
If you have any questions, please let me know:
- Liyuan Zhu {[email protected]}
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: