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.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2022 - 17th European Conference Tel Aviv, Israel, October 23–27, 2022 Proceedings, Part XXXI |
Editors | Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 78-96 |
Number of pages | 19 |
ISBN (Electronic) | 9783031198212 |
ISBN (Print) | 9783031198205 |
DOIs | |
Publication status | Published - 2022 |
Event | European Conference on Computer Vision 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022 Conference number: 17th https://link.springer.com/book/10.1007/978-3-031-19830-4 (Proceedings) https://eccv2022.ecva.net (Website) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 13691 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | European Conference on Computer Vision 2022 |
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Abbreviated title | ECCV 2022 |
Country/Territory | Israel |
City | Tel Aviv |
Period | 23/10/22 → 27/10/22 |
Internet address |
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Keywords
- Dual adaptive transformations
- Point cloud segmentation
- Weakly supervised segmentation