This repository contains the code (in PyTorch) for "EFGHNet: A Versatile Image-to-Point Cloud Registration Network for Extreme Outdoor Environment" paper (IROS 2022).
- Python 3.8
- PyTorch 1.10
- RELLIS-3D dataset
conda create -n efgh python=3.8
conda activate efgh
pip install -r requirements.txt
cd lib
python build_khash_cffi.py
cd ..
Download RELLIS-3D dataset from https://unmannedlab.github.io/research/RELLIS-3D
data
└── RELLIS-3D
├── RELLIS-3D
| ├── 00000
| | ├── os1_cloud_node_kitti_bin
| | ├── pylon_camera_node
| | ├── calib.txt
| | ├── poses.txt
| | └── camera_info.txt
| ├── 00001
| └── ..
├── RELLIS_3D
| ├── 00000
| | └── transforms.yaml
| ├── 00001
| └── ..
├── pt_test.lst
├── pt_train.lst
└── pt_val.lst
Set data_root and ckpt_dir in the train_rellis.yaml file.
python main.py configs/train_rellis.yaml
Set ckpt_path in the test_rellis.yaml file.
python main.py configs/test_rellis.yaml
April 2023 update
Please check the config.yaml file to set the parameters before using the pretrained model: Download link
Our BCL implementation is based on https://github.com/laoreja/HPLFlowNet.
If you use our code or method in your work, please cite the following:
@article{jeon2022efghnet,
title={EFGHNet: A Versatile Image-to-Point Cloud Registration Network for Extreme Outdoor Environment},
author={Jeon, Yurim and Seo, Seung-Woo},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={3},
pages={7511--7517},
year={2022},
publisher={IEEE}
}
Please direct any questions to Yurim Jeon at [email protected]