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SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Labelling point clouds fully is highly time-consuming and costly. As larger point cloud datasets with billions of points become more common, we ask whether the full annotation is even necessary, demonstrating that existing baselines designed under a fully annotated assumption only degrade slightly even when faced with 1% random point annotations. However, beyond this point, e.g., at 0.1% annotations, segmentation accuracy is unacceptably low. We observe that, as point clouds are samples of the 3D world, the distribution of points in a local neighbourhood is relatively homogeneous, exhibiting strong semantic similarity. Motivated by this, we propose a new weak supervision method to implicitly augment highly sparse supervision signals. Extensive experiments demonstrate the proposed Semantic Query Network (SQN) achieves promising performance on seven large-scale open datasets under weak supervision schemes, while requiring only 0.1% randomly annotated points for training, greatly reducing annotation cost and effort.

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Notes

  1. 1.

    https://github.com/liuzhengzhe/One-Thing-One-Click/issues/13.

  2. 2.

    https://github.com/liuzhengzhe/One-Thing-One-Click/issues/8.

  3. 3.

    https://www.cloudcompare.org/.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 61972435, U20A20185), China Scholarship Council (CSC) scholarship, and Huawei UK AI Fellowship. Qingyong Hu and Bo Yang were partially supported by Shenzhen Science and Technology Innovation Commission (JCYJ20210324120603011).

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Authors and Affiliations

  1. University of Oxford, Oxford, UK

    Qingyong Hu, Niki Trigoni & Andrew Markham

  2. vLAR Group, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Bo Yang

  3. Sun Yat-sen University, Guangzhou, China

    Guangchi Fang & Yulan Guo

  4. Huawei Noah’s Ark Lab, Birmingham, UK

    Aleš Leonardis

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  1. Qingyong Hu
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  2. Bo Yang
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  3. Guangchi Fang
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  4. Yulan Guo
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Correspondence to Bo Yang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Hu, Q. et al. (2022). SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13687. Springer, Cham. https://doi.org/10.1007/978-3-031-19812-0_35

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