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RGM: Robust Point Cloud Registration Framework Based on Deep Graph Matching (CVPR2021)

This repository provides code and data required to train and evaluate RGM. It represents the official implementation of the paper:

Robust Point Cloud Registration Framework Based on Deep Graph Matching

Kexue Fu, Shaolei Liu, Xiaoyuan Luo, Manning Wang

Instructions

This code has been tested on

Requirements

To create a virtual environment and install the required dependences please run:

git clone https://github.com/fukexue/RGM.git
conda create -n RGM
conda activate RGM
pip install -r requirements.txt

in your working folder.

Note: If you want to get the same results as in the paper, install numpy.version=='1.19.2' and scipy.version=='1.5.0'.

Datasets and pretrained models

For ModelNet40, the data will be downloaded automatically.

For ShapeNet dataset, please download it from this link ShapeNet. Unzip, named 'shapenet_raw' and place it in the data folder.

We provide

Train

sh 1_experiment_train.sh

Eval

sh 1_experiment_eval.sh

Citation

If you find this code useful for your work or use it in your project, please consider citing:

@article{Fu2021RGM,
  title={Robust Point Cloud Registration Framework Based on Deep Graph Matching},
  author={Kexue Fu, Shaolei Liu, Xiaoyuan Luo, Manning Wang},
  journal={Internaltional Conference on Computer Vision and Pattern Recogintion (CVPR)},
  year={2021}
}

Acknowledgments

In this project we use (parts of) the official implementations of the followin works:

We thank the respective authors for open sourcing their methods.