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

Mengtian Li, Yuan Xie, Yunhang Shen, Bo Ke, Ruizhi Qiao, Bohan Ren, Shaohui Lin, Lizhuang Ma
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引用次数: 28

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.
基于混合对比正则化的弱监督三维点云语义分割
为了解决大规模点云语义分割中巨大的标注成本问题,我们提出了一种新的弱监督环境下的混合对比正则化(HybridCR)框架,该框架与完全监督环境下的混合对比正则化框架相比具有竞争力。具体来说,HybridCR是第一个以端到端方式利用点一致性和使用带有伪标记的对比正则化的框架。从根本上说,HybridCR明确有效地考虑了局部相邻点之间的语义相似性和3D类的全局特征。我们进一步设计了一个动态点云增强器来生成多样性和鲁棒性的样本视图,并将其转换参数与模型训练相结合进行优化。通过大量的实验,HybridCR在室内和室外数据集(如S3DIS、ScanNet-V2、Semantic3D和SemanticKITTI)上都比SOTA方法取得了显著的性能提升。
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