datasets | ||
filenames | ||
models | ||
scripts | ||
utils | ||
.gitignore | ||
LICENSE | ||
main.py | ||
README.md | ||
save_disp.py |
GwcNet
This is the implementation of the paper Group-wise Correlation Stereo Network, CVPR 19, Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li [Arxiv]
How to use
Environment
- python 3.6
- Pytorch >= 0.4.1
Data Preparation
Download Scene Flow Datasets, KITTI 2012, KITTI 2015
Training
Scene Flow Datasets
run the script ./scripts/sceneflow.sh
to train on Scene Flow datsets. Please update DATAPATH
in the bash file as your training data path.
KITTI 2012 / 2015
run the script ./scripts/kitti12.sh
and ./scripts/kitti15.sh
to finetune on the KITTI 12/15 dataset. Please update DATAPATH
and --loadckpt
as your training data path and pretrained SceneFlow checkpoint file.
Evaluation
run the script ./scripts/kitti12_save.sh
and ./scripts/kitti15_save.sh
to save png predictions on the test set of the KITTI datasets to the folder ./predictions
.
Pretrained Models
Citation
If you find this code useful in your research, please cite:
@inproceedings{guo2019group,
title={Group-wise correlation stereo network},
author={Guo, Xiaoyang and Yang, Kai and Yang, Wukui and Wang, Xiaogang and Li, Hongsheng},
booktitle={CVPR},
year={2019}
}
Acknowledgements
Thanks to Jia-Ren Chang for opening source of his excellent work PSMNet. Our work is inspired by this work and part of codes in models
are migrated from PSMNet.