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.
We update the settings of RegFormer on the Nuscenes dataset in RegFormer_NuScenes branch.
Our model only depends on the following commonly used packages.
| Package | Version |
|---|---|
| CUDA | 1.11.3 |
| Python | 3.8.10 |
| PyTorch | 1.12.0 |
| h5py | not specified |
| tqdm | not specified |
| numpy | not specified |
| openpyxl | not specified |
Device: NVIDIA RTX 3090
Compile the furthest point sampling, grouping and gathering operation for PyTorch with following commands.
cd pointnet2
python setup.py installWe 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 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
├── ...
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
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.
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.pyPlease 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.
@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}
}
We thank the following open-source project for the help of the implementations: