/Pseudo-LiDAR-for-Visual-Odometry

Primary LanguagePython

Pseudo-LiDAR-for-Visual-Odometry

TIM2023 "Pseudo-LiDAR for Visual Odometry" created by Yanzi Miao, Huiying Deng, Chaokang Jiang, Zhiheng Feng, Xinrui Wu, Guangming Wang, and Hesheng Wang.

Installation

Our model only depends on the following commonly used packages.

Package Version
CUDA 11.3
PyTorch 1.10.0
h5py not specified
tqdm not specified
numpy not specified
openpyxl not specified

Device: NVIDIA RTX 3090

Install the pointnet2 library

Compile the furthest point sampling, grouping and gathering operation for PyTorch with following commands.

cd ops_pytorch
cd fused_conv_random_k
python setup.py install
cd ../
cd fused_conv_select_k
python setup.py install
cd ../

Datasets

KITTI Dataset

Datasets are available at KITTI Odometry benchmark website: https://drive.google.com/drive/folders/1Su0hCuGFo1AGrNb_VMNnlF7qeQwKjfhZ The data of the KITTI odometry dataset should be organized as follows:

data_root
├── 00
│   ├── velodyne
│   ├── calib.txt
├── 01
├── ...

Training

Train the network by running :

python train.py

Please reminder to specify the GPU, data_root,log_dir, train_list(sequences for training), val_list(sequences for validation). You may specify the value of arguments. Please find the available arguments in the configs.py.

Testing

Our network is evaluated every 5 epoph during training. If you only want the evaluation results, you can set the parameter 'eval_before' as 'True' in file config.py, then evaluate the network by running :

python train.py

Please reminder to specify the GPU, data_root,log_dir, test_list(sequences for testing) in the scripts. You can also get the pretrined model in https://drive.google.com/file/d/13xQGTcsO0YwBYbcQTSHk7BkNrOLG18mX/view?usp=drive_link.

Quantitative results:

KITTI

Citation

@article{deng2022pseudo,
  title={Pseudo-LiDAR for Visual Odometry},
  author={Miao, Yanzi and Deng, Huiying and Jiang, Chaokang and Feng, Zhiheng and  and Wu, Xinrui and Wang, Guangming and Wang, Hesheng},
  journal={IEEE Transactions on Instrumentation and Measurement},
  year={2022}
}

Acknowledgments

We thank the following open-source project for the help of the implementations: