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Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13691))

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Abstract

Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in this paper, we advocate applying the consistency constraint under various perturbations to effectively regularize unlabeled 3D points. Specifically, we propose a novel DAT (Dual Adaptive Transformations) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate that our model can effectively leverage the unlabeled 3D points and achieve significant performance gains on both datasets, setting new state-of-the-art performance for weakly supervised point cloud segmentation.

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Acknowledgments

This study is supported under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This research is partly supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2018-003), the Ministry of Education, Singapore, under its Academic Research Fund Tier 2 (MOE-T2EP20220-0007) and Tier 1 (RG95/20). This research is also partially supported by Monash FIT Start-up Grant and SenseTime Gift Fund.

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Authors and Affiliations

  1. S-Lab, Nanyang Technological University, Singapore, Singapore

    Zhonghua Wu & Guosheng Lin

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Zhonghua Wu, Guosheng Lin & Jianfei Cai

  3. Department of Data Science and AI, Monash University, Melbourne, Australia

    Yicheng Wu & Jianfei Cai

  4. SenseTime Research, Shanghai, China

    Chen Qian

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  1. Zhonghua Wu
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  2. Yicheng Wu
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  3. Guosheng Lin
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  4. Jianfei Cai
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  5. Chen Qian
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Corresponding author

Correspondence to Guosheng Lin .

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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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Wu, Z., Wu, Y., Lin, G., Cai, J., Qian, C. (2022). Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation. 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 13691. Springer, Cham. https://doi.org/10.1007/978-3-031-19821-2_5

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