• A fast and robust ground segmentation algorithm on 3D point cloud.

• An extension of Patchwork (RA-L'21 with IROS'21).

• Please refer our paper for detailed explanantions and experimental results!

  • Validated on SemanticKITTI dataset. The benchmark code is available on here.

• 💡 Contents: YouTube

• C++ source code of Patchwork++ (patchworkpp)
• Python binding of Patchwork++ using pybind11 (python_wrapper)
• Examples codes, which visualizes a ground segmentation result by Patchwork++ (examples) 👍

If you are familiar with ROS, you can also visit here and try executing ROS-based Patchwork++!

You may need to install Eigen, numpy, and Open3D. Open3D is used for point cloud visualization.

# To install Eigen and numpy
$ sudo apt-get install libeigen3-dev
$ pip install numpy

# To install Open3D Python packages
$ pip install open3d

# To install Open3D C++ packages
$ git clone https://github.com/isl-org/Open3D
$ cd Open3D
$ util/install_deps_ubuntu.sh # Only needed for Ubuntu
$ mkdir build && cd build
$ cmake ..
$ make
$ sudo make install

Please follow below codes to build Patchwork++.

$ git clone https://github.com/url-kaist/patchwork-plusplus
$ cd patchwork-plusplus
$ mkdir build && cd build
$ cmake ..
$ make

There are some example codes for your convenience! Please try using Patchwork++ to segment ground points in a 3D point cloud 😃

# Run patchwork++ and visualize ground points(green) and nonground points(red)
$ python examples/python/demo_visualize.py

# Run patchwork++ with sequential point cloud inputs 
$ python examples/python/demo_sequential.py

# Run patchwork++ and visualize ground points(green) and nonground points(red)
$ ./examples/cpp/demo_visualize

# Run patchwork++ with sequential point cloud inputs 
$ ./examples/cpp/demo_sequential

If you execute Patchwork++ with given demo codes well, you can get the following result!

It is a ground segmentation result of data/000000.bin file using Open3D visualization. (Ground : Green, Nonground : Red)

If you use our codes, please cite our paper (arXiv)

@inproceedings{lee2022patchworkpp,
    title={{Patchwork++: Fast and robust ground segmentation solving partial under-segmentation using 3D point cloud}},
    author={Lee, Seungjae and Lim, Hyungtae and Myung, Hyun},
    booktitle={Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst.},
    year={2022},
    note={{Submitted}} 
}

In addition, you can also check the paper of our baseline(Patchwork) here.

@article{lim2021patchwork,
    title={Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor},
    author={Lim, Hyungtae and Minho, Oh and Myung, Hyun},
    journal={IEEE Robotics and Automation Letters},
    year={2021}
}

If you have any questions, please do not hesitate to contact us

• Seungjae Lee ✉️ sj98lee at kaist.ac.kr
• Hyungtae Lim ✉️ shapelim at kaist.ac.kr