Awesome

RegFormer

ICCV2023 "RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration" created by Jiuming Liu, Guangming Wang, Zhe Liu, Chaokang Jiang, Marc Pollofeys, and Hesheng Wang. <img src="pipeline2.png">

NEWS

We update the settings of RegFormer on the Nuscenes dataset in RegFormer_NuScenes branch.

Installation

Our model only depends on the following commonly used packages.

Device: NVIDIA RTX 3090

Install the pointnet2 library

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

cd pointnet2
python setup.py install

Install the CUDA-based KNN searching and random searching

We leverage CUDA-based operator for parallel neighbor searching [Reference: [EfficientLONet] (https://github.com/IRMVLab/EfficientLO-Net)]. You can compile them 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
├── ...

NuScenes Dataset

The data of the NuScenes odometry dataset (https://nuscenes.org/nuscenes#download) should be organized as follows:

DATA_ROOT
├── v1.0-trainval
│   ├── maps
│   ├── samples
│   │   ├──LIDAR_TOP
│   ├── sweeps
│   ├── v1.0-trainval
├── v1.0-test
│   ├── maps
│   ├── samples
│   │   ├──LIDAR_TOP
│   ├── sweeps
│   ├── v1.0-test

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 2 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/drive/folders/1epQUIxG4wIg2yJu7kxArrwOmE0B24OeV.

Quantitative results:

KITTI

NuScenes

Citation

@InProceedings{Liu_2023_ICCV,
    author    = {Liu, Jiuming and Wang, Guangming and Liu, Zhe and Jiang, Chaokang and Pollefeys, Marc and Wang, Hesheng},
    title     = {RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {8451-8460}
}

Acknowledgments

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

• PointNet++
• KITTI_odometry_evaluation_tool
• [PWCLONet] (https://github.com/IRMVLab/PWCLONet)
• [HRegNet] (https://github.com/ispc-lab/HRegNet)