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A Dynamic Points Removal Benchmark in Point Cloud Maps

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Here is a preview of the readme in codes. Task detects dynamic points in maps and removes them, enhancing the maps:

<center> <img src="assets/imgs/background.png" width="80%"> </center>

Folder quick view:

Quick try:

📓 Visit our wiki page for detailed tutorials and updates.

News:

Feel free to pull a request if you want to add more methods or datasets. Welcome! We will try our best to update methods and datasets in this benchmark. Please give us a star 🌟 and cite our work 📖 if you find this useful for your research. Thanks!

Methods:

Please check in methods folder.

Online (w/o prior map):

Learning-based (data-driven) (w pretrain-weights provided):

Offline (need prior map).

Please note that we provided the comparison methods also but modified a little bit for us to run the experiments quickly, but no modified on their methods' core. Please check the LICENSE of each method in their official link before using it.

You will find all methods in this benchmark under methods folder. So that you can easily reproduce the experiments. Or click here to check our score screenshot directly.

<!-- And we will also directly provide [the result data](TODO) so that you don't need to run the experiments by yourself. ... Where to save this? -->

Last but not least, feel free to pull request if you want to add more methods. Welcome!

Dataset & Scripts

Download PCD files mentioned in paper from Zenodo online drive. Or create unified format by yourself through the scripts we provided for more open-data or your own dataset. Please follow the LICENSE of each dataset before using it.

Welcome to contribute your dataset with ground truth to the community through pull request.

Evaluation

First all the methods will output the clean map, if you are only user on map clean task, it's enough. But for evaluation, we need to extract the ground truth label from gt label based on clean map. Why we need this? Since maybe some methods downsample in their pipeline, we need to extract the gt label from the downsampled map.

Check create dataset readme part in the scripts folder to get more information. But you can directly download the dataset through the link we provided. Then no need to read the creation; just use the data you downloaded.

Acknowledgements

This benchmark implementation is based on codes from several repositories as we mentioned in the beginning. Thanks for these authors who kindly open-sourcing their work to the community. Please see our paper reference section to get more information.

Thanks to HKUST Ramlab's members: Bowen Yang, Lu Gan, Mingkai Tang, and Yingbing Chen, who help collect additional datasets.

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation

Cite Our Papers

Please cite our works if you find these useful for your research:

@inproceedings{zhang2023benchmark,
  author={Zhang, Qingwen and Duberg, Daniel and Geng, Ruoyu and Jia, Mingkai and Wang, Lujia and Jensfelt, Patric},
  booktitle={IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={A Dynamic Points Removal Benchmark in Point Cloud Maps}, 
  year={2023},
  pages={608-614},
  doi={10.1109/ITSC57777.2023.10422094}
}
@article{jia2024beautymap,
  author={Jia, Mingkai and Zhang, Qingwen and Yang, Bowen and Wu, Jin and Liu, Ming and Jensfelt, Patric},
  journal={IEEE Robotics and Automation Letters}, 
  title={{BeautyMap}: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps}, 
  year={2024},
  volume={9},
  number={7},
  pages={6256-6263},
  doi={10.1109/LRA.2024.3402625}
}
@article{daniel2024dufomap,
  author={Duberg, Daniel and Zhang, Qingwen and Jia, Mingkai and Jensfelt, Patric},
  journal={IEEE Robotics and Automation Letters}, 
  title={{DUFOMap}: Efficient Dynamic Awareness Mapping}, 
  year={2024},
  volume={9},
  number={6},
  pages={5038-5045},
  doi={10.1109/LRA.2024.3387658}
}