Awesome
LCCNet
Official PyTorch implementation of the paper “LCCNet: Lidar and Camera Self-Calibration Using Cost Volume Network”. A video of the demonstration of the method can be found on https://www.youtube.com/watch?v=UAAGjYT708A
Table of Contents
Requirements
- python 3.6 (recommend to use Anaconda)
- PyTorch==1.0.1.post2
- Torchvision==0.2.2
- Install requirements and dependencies
pip install -r requirements.txt
Pre-trained model
Pre-trained models can be downloaded from google drive
Evaluation
- Download KITTI odometry dataset.
- Change the path to the dataset in
evaluate_calib.py
.
data_folder = '/path/to/the/KITTI/odometry_color/'
- Create a folder named
pretrained
to store the pre-trained models in the root path. - Download pre-trained models and modify the weights path in
evaluate_calib.py
.
weights = [
'./pretrained/kitti_iter1.tar',
'./pretrained/kitti_iter2.tar',
'./pretrained/kitti_iter3.tar',
'./pretrained/kitti_iter4.tar',
'./pretrained/kitti_iter5.tar',
]
- Run evaluation.
python evaluate_calib.py
Train
python train_with_sacred.py
Citation
Thank you for citing our paper if you use any of this code or datasets.
@article{lv2020lidar,
title={Lidar and Camera Self-Calibration using CostVolume Network},
author={Lv, Xudong and Wang, Boya and Ye, Dong and Wang, Shuo},
journal={arXiv preprint arXiv:2012.13901},
year={2020}
}
Acknowledgments
We are grateful to Daniele Cattaneo for his CMRNet github repository. We use it as our initial code base.
<!-- [correlation_package](models/LCCNet/correlation_package) was taken from [flownet2](https://github.com/NVIDIA/flownet2-pytorch/tree/master/networks/correlation_package) [LCCNet.py](model/LCCNet.py) is a modified version of the original [PWC-DC network](https://github.com/NVlabs/PWC-Net/blob/master/PyTorch/models/PWCNet.py) and modified version [CMRNet](https://github.com/cattaneod/CMRNet/blob/master/models/CMRNet/CMRNet.py) -->