HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization

Mengtian Li, Yuan Xie, Yunhang Shen, Bo Ke, Ruizhi Qiao, Bo Ren, Shaohui Lin, Lizhuang Ma

CVPR 2022 pp. 14930-14939

doi:10.1109/CVPR52688.2022.01451 /cvpr/2022/li2022cvpr-hybridcr/

Abstract

To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is the first framework to leverage both point consistency and employ contrastive regularization with pseudo labeling in an end-to-end manner. Fundamentally, HybridCR explicitly and effectively considers the semantic similarity between local neighboring points and global characteristics of 3D classes. We further design a dynamic point cloud augmentor to generate diversity and robust sample views, whose transformation parameter is jointly optimized with model training. Through extensive experiments, HybridCR achieves significant performance improvement against the SOTA methods on both indoor and outdoor datasets, e.g., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI.

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Cite

Text

Li et al. "HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.01451

Markdown

[Li et al. "HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/li2022cvpr-hybridcr/) doi:10.1109/CVPR52688.2022.01451

BibTeX

@inproceedings{li2022cvpr-hybridcr,
  title     = {{HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization}},
  author    = {Li, Mengtian and Xie, Yuan and Shen, Yunhang and Ke, Bo and Qiao, Ruizhi and Ren, Bo and Lin, Shaohui and Ma, Lizhuang},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2022},
  pages     = {14930-14939},
  doi       = {10.1109/CVPR52688.2022.01451},
  url       = {https://mlanthology.org/cvpr/2022/li2022cvpr-hybridcr/}
}