Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving

This repository contains the implementation of our paper.

Installation

Install this package by running in the root directory of this repo:

pip3 install -U -e .

Install the packages in requirements.txt.

Data preparation

SemanticKITTI

Download the SemanticKITTI dataset inside the directory data/kitti/. The directory structure should look like this:

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

NuScenes

We use nuscenes2kitti to convert the nuScenes format into the SemanticKITTI format and store it in data/nuscenes/.

In the scripts, use the --nuscenes flag to train or evaluate using this dataset.

Pretrained models

Reproducing results

python3 scripts/evaluate_model.py --w [path_to_model]

Training

python3 scripts/train_model.py

Citation

@article{marcuzzi2023ral,
  author = {R. Marcuzzi and L. Nunes and L. Wiesmann and J. Behley and C. Stachniss},
  title = {{Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving}},
  journal = ral,
  volume = {8},
  number = {2},
  pages = {1141--1148},
  year = 2023,
  doi = {10.1109/LRA.2023.3236568},
  url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2023ral.pdf},
}

Licence

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

PRBonn/MaskPLS