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arXiv

SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels

This is the official repository of the Semantic Query Network (SQN). For technical details, please refer to:

SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels
Qingyong Hu, Bo Yang, Guangchi Fang, Ales Leonardis, Yulan Guo, Niki Trigoni, Andrew Markham.
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Citation

If you find our work useful in your research, please consider citing:

@article{hu2021sqn,
  title={SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels},
  author={Hu, Qingyong and Yang, Bo and Fang, Guangchi and Guo, Yulan and Leonardis, Ales and Trigoni, Niki and Markham, Andrew},
  journal={arXiv preprint arXiv:2104.04891},
  year={2021}
}

Related Repos

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  3. 3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds GitHub stars
  4. SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration GitHub stars
  5. SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds GitHub stars

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