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3DUIS: 3D Unsupervised Instance Segmentation

This repo contains the code for our paper: Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles.

Our approach uses a self-supervised pretrained network to extract point-wise features and use it to build a graph representation of the point cloud, mapping the relation between each point and it's neighbors. Then, we apply GraphCut to divide each instance from the background, achieving class-agnostic instance segmentation in an unsupervised manner.

Additionally, we created a new benchmark for Open-World LiDAR instance segmentation based on SemanticKITTI here.

Table of Contents

1. Introduction of the paper and benchmark
2. Publication
3. Dependencies
4. Data preparation
5. Pretrained weights
6. Running the code
7. License

Publication

If you use our code and benchmark, please cite the correspondig papers:

@article{nunes2022ral,
    author = {L. Nunes and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
    title = {{SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination}},
    journal = {{IEEE Robotics and Automation Letters (RA-L)}},
    year = 2022,
    doi = {10.1109/LRA.2022.3142440},
    issn = {2377-3766},
    volume = {7},
    number = {2},
    pages = {2116-2123},
    url = {http://www.ipb.uni-bonn.de/pdfs/nunes2022ral-icra.pdf},
}

@article{nunes2022ral-3duis,
  author = {Lucas Nunes and Xieyuanli Chen and Rodrigo Marcuzzi and Aljosa Osep and Laura Leal-Taixé and Cyrill Stachniss and Jens Behley},
  title = {{Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles}},
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  year = 2022,
  doi = {10.1109/LRA.2022.3187872}},
  issn = {2377-3766},
  volume = {7},
  number = {4},
  pages = {8713-8720},
  url = {https://www.ipb.uni-bonn.de/pdfs/nunes2022ral-iros.pdf},
}

Dependencies

Installing pre-requisites:

sudo apt install build-essential python3-dev libopenblas-dev

pip3 install -r requirements.txt

Next install MinkowskiEngine:

pip3 install -U git+https://github.com/NVIDIA/MinkowskiEngine --install-option="--blas=openblas" -v --no-deps

Data preparation

Download SemanticKITTI inside the directory ./Datasets/SemanticKITTI/datasets. The directory structure should be:

./
└── Datasets/
    └── SemanticKITTI
        └── dataset
          └── sequences
            ├── 00/           
            │   ├── velodyne/	
            |   |	├── 000000.bin
            |   |	├── 000001.bin
            |   |	└── ...
            │   └── labels/ 
            |       ├── 000000.label
            |       ├── 000001.label
            |       └── ...
            ├── 08/ # for validation
            ├── 11/ # 11-21 for testing
            └── 21/
                └── ...

For the unsupervised ground segmentation, you need to run patchwork over the SemanticKITTI dataset and put the generated files over:

./
└── Datasets/
    └── SemanticKITTI
        └── assets
            └── patchwork   
                ├── 08
                    ├── 000000.label
                    ├── 000001.label
                    └── ...

For the validation set (sequence 08) you can download the patchwork ground segmentation here and extract as described above.

Pretrained weights

You can download here the network weights pretrained with SegContrast , and it should be extracted inside ./checkpoints.

Running the code

To extract the instances run:

python3 3duis.py

(In the repo we have one example point cloud that you can run to see the results and check if the setup is working)

The predicted instance segmentation should be saved in outputs/3DUIS/

To visualize it run:

python3 vis_inst.py

License

This project is free software made available under the MIT License. For details see the LICENSE file.