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PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

This repository represents the official implementation of the paper:

PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

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/phdymz/PointMBF.git
conda create --name PointMBF python=3.8
conda activate PointMBF
pip install -r requirements.txt

Make dataset

You need to download the RGB-D version of 3DMatch dataset and ScanNet dataset in advance. Details can refer to URR.

3DMatch

python create_3dmatch_rgbd_dict.py --data_root 3dmatch_train.pkl train
python create_3dmatch_rgbd_dict.py --data_root 3dmatch_valid.pkl valid
python create_3dmatch_rgbd_dict.py --data_root  3dmatch_test.pkl test

ScanNet

python create_scannet_dict.py --data_root scannet_train.pkl train
python create_scannet_dict.py --data_root scannet_valid.pkl valid
python create_scannet_dict.py --data_root scannet_test.pkl test

Train on 3DMatch

python train.py --name RGBD_3DMatch  --RGBD_3D_ROOT

Train on ScanNet

python train.py --name ScanNet  --SCANNET_ROOT

Inference

python test.py --checkpoint --SCANNET_ROOT

Pretrained Model

We provide the pre-trained model of PointMBF in BaiDuyun, Password: pmbf.

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.

Citation

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

@article{yuan2023pointmbf,
  title={PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration},
  author={Yuan, Mingzhi and Fu, Kexue and Li, Zhihao and Meng, Yucong and Wang, Manning},
  journal={arXiv preprint arXiv:2308.04782},
  year={2023}
}