Awesome

HypLiLoc

(CVPR 2023) HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion

You can also view at https://youtu.be/qplZMOZG-7k

💥💥:racehorse::racehorse: We have refined the code structure. This new version can run at 80FPS on NVIDIA 3090 GPU or 150FPS on NVIDIA 4090 GPU!

Requirements

PyTorch installation (You may also use Pytorch2.0, which is also compatible):

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

You can also use the following script:

conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

Other dependencies:

colour_demosaicing==0.2.2
geotorch==0.3.0
matplotlib==3.5.3
numpy==1.19.5
open3d==0.15.2
opencv_python==4.6.0.66
Pillow==9.3.0
scipy==1.9.1
setuptools==63.4.1
tqdm==4.64.0
transforms3d==0.4.1

Extra dependency pointnet2 installation

cd network/pointnet2
python setup.py install
cd ..
cd ..

Platform
```
Ubuntu 20.04
CUDA 11.6/11.8
python 3.8
```
Dataset

We currently provide the Oxford Radar dataset that has been pre-processed.

google drive

After downloading, you can unzip it and record the path, e.g.
```
/home/workstation/Radar_RobotCar/
```
Tips: You can better put the dataset under some folder supported by SSD to achieve fast reading speed.
Trained weights

We provide the trained optimal weights for the Full-8 route.

https://drive.google.com/file/d/1xunKg82BK2-yOyh7q04AOxL6qL5VKEQE/view?usp=share_link

After downloading, you can unzip it and put it under this repo's root, which will be like:
```
HypLiLoc/logs
```
Inference on the Full-8 route

We have refined the code structure, and the version can run at 140FPS !
```
python eval.py --data_dir /home/workstation/Radar_RobotCar/ --cuda 0 --scene full8 
```

Train

python train.py --data_dir /home/workstation/Radar_RobotCar/ --cuda 0 --scene full8

Other information:

You can view tools/options.py to set running arguments.

Citation

@inproceedings{wang2023hypliloc,
  title={HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion},
  author={Wang, Sijie and Kang, Qiyu and She, Rui and Wang, Wei and Zhao, Kai and Song, Yang and Tay, Wee Peng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5176--5185},
  year={2023}
}

Code reference

https://github.com/sijieaaa/RobustLoc

https://github.com/htdt/hyp_metric

https://github.com/BingCS/AtLoc