华东师范大学学报（自然科学版）Issue(2)：108-118,11.DOI:10.3969/j.issn.1000-5641.2024.02.012
基于组对比学习的弱监督三维点云语义分割方法
Group contrastive learning for weakly-supervised 3D point cloud semantic segmentation
郑智鸿 1宋海川1
作者信息
- 1. 华东师范大学计算机科学与技术学院,上海 200062
- 折叠
摘要
Abstract
Three-dimensional point cloud semantic segmentation is an essential task for 3D visual perception and has been widely used in autonomous driving,augmented reality,and robotics.However,most methods work under a fully-supervised setting,which heavily relies on fully annotated datasets.Many weakly-supervised methods have utilized the pseudo-labeling method to retrain the model and reduce the labeling time consumption.However,the previous methods have failed to address the conformation bias induced by false pseudo labels.In this study,we proposed a novel weakly-supervised 3D point cloud semantic segmentation method based on group contrastive learning,constructing contrast between positive and negative sample groups selected from pseudo labels.The pseudo labels will compete with each other within the group contrastive learning,reducing the gradient contribution of falsely predicted pseudo labels.Results on three large-scale datasets show that our method outperforms state-of-the-art weakly-supervised methods with minimal labeling annotations and even surpasses the performance of some classic fully-supervised methods.关键词
弱监督学习/三维点云/语义分割/对比学习Key words
weakly-supervised learning/3D point cloud/semantic segmentation/contrastive learning分类
信息技术与安全科学引用本文复制引用
郑智鸿,宋海川..基于组对比学习的弱监督三维点云语义分割方法[J].华东师范大学学报（自然科学版）,2024,(2):108-118,11.
