Point cloud semantic segmentation is significant for managing and protecting architectural heritage. Currently, fully supervised methods require a large amount of annotated data, while weakly supervised methods are difficult to transfer directly to architectural heritage. This paper proposes an end-to-end teacher-guided consistency and contrastive learning weakly supervised (TCCWS) framework for architectural heritage point cloud semantic segmentation, which can fully utilize limited labeled points to train network. Specifically, a teacher-student framework is designed to generate pseudo labels and a pseudo label dividing module is proposed to distinguish reliable and ambiguous point sets. Based on it, a consistency and contrastive learning strategy is designed to fully utilize supervision signals to learn the features of point clouds. The framework is tested on the ArCH dataset and self-collected point cloud, which demonstrates that the proposed method can achieve effective semantic segmentation of architectural heritage using only 0.1 % of annotated points.

中文翻译：

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使用教师指导的一致性和对比学习对建筑遗产进行弱监督 3D 点云语义分割


点云语义分割对于管理和保护建筑遗产非常重要。目前，完全监督的方法需要大量的注释数据，而弱监督方法很难直接转移到建筑遗产中。本文提出了一种端到端教师指导的一致性和对比学习弱监督 （TCCWS） 框架，用于建筑遗产点云语义分割，该框架可以充分利用有限的标记点来训练网络。具体来说，设计了一个师生框架来生成伪标签，并提出了一个伪标签划分模块来区分可靠和模糊的点集。基于此，设计了一种一致性和对比性学习策略，以充分利用监督信号来学习点云的特征。该框架在 ArCH 数据集和自收集的点云上进行了测试，这表明所提出的方法只需使用 0.1% 的注释点即可实现对建筑遗产的有效语义分割。