Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning
发布时间：2025-01-07 点击次数：
DOI码：10.1016/j.autcon.2024.105831
所属单位：School of Remote Sensing and Information Engineering, Wuhan University, China
发表刊物：AUTOMATION IN CONSTRUCTION
关键字：Point cloud,Architectural heritage,3D semantic segmentation,Weakly supervised
摘要：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 teacherstudent 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.
合写作者： Pengcheng,Zhao, Mingyao,Ai, Qingwu, Shuowen,Huang, Shaohua,Wang, Hao,Cui, Jian,Li,Hu
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：168
ISSN号：0926-5805
是否译文：否
CN号：EI:20244217207097,WOS:001337166400001,Scopus:2-s2.0-85206269815
发表时间：2024-12-01
