Skip to content

Latest commit

 

History

History
84 lines (64 loc) · 2.52 KB

README.md

File metadata and controls

84 lines (64 loc) · 2.52 KB

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. Uploading image.png…

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

image

Acknowledgement

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

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}
}