Global point cloud registration based on SphereNet and TEASER++

SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration.

Overview

TEASER++: ast & certifiable 3D registration.

Setup

This code has been tested with

1. Python 3.9, Pytorch 1.11.0, CUDA 10.2 on Arch Linux.
2. Python 3.9, Pytorch 1.11.0, CUDA 11.1 on Ubuntu 20.04.

• Clone the repository

git clone https://github.com/GuiyuZhao/SphereNet && cd SphereNet

• Setup conda virtual environment

conda create -n spherenet python=3.9
source activate spinnet
conda install pytorch==1.11.0 torchvision==0.12.0 cudatoolkit=11.3 -c pytorch
conda install -c open3d-admin open3d==0.11.1
pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"

• Setup Teaser++

sudo apt install cmake libeigen3-dev libboost-all-dev
conda create -n teaser_test python=3.6 numpy
conda activate teaser_test
conda install -c open3d-admin open3d=0.9.0.0
git clone https://github.com/MIT-SPARK/TEASER-plusplus.git
cd TEASER-plusplus && mkdir build && cd build
cmake -DTEASERPP_PYTHON_VERSION=3.6 .. && make teaserpp_python
cd python && pip install .
cd ../.. && cd examples/teaser_python_ply 
python teaser_python_ply.py

• Prepare the datasets

Demo: Global point cloud registration using SphereNet and TEASER++

If necessary, you will need to change the radius parameter to fit your data.

cd ./ThreeDMatch/Test
python demo.py  

Acknowledgement

In this project, we use parts of the implementations of the following works:

• SphereNet
• TEASER++

Updates

• 03/05/2023: The code is released!
• 12/13/2023: Our paper is accepted by IEEE Transactions on Geoscience and Remote Sensing!

Citation

@ARTICLE{10356130,
  author={Zhao, Guiyu and Guo, Zhentao and Wang, Xin and Ma, Hongbin},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration}, 
  year={2024},
  volume={62},
  number={},
  pages={1-16},
  doi={10.1109/TGRS.2023.3342423}
}