三维点云弱监督语义分割研究进展综述
A review of research progress in weakly supervised semantic segmentation of 3D point clouds
伍婕 1张安思 1李松 1张保 2张仪宗2
作者信息
- 1. 贵州大学省部共建公共大数据国家重点实验室,贵阳 550025
- 2. 贵州大学机械工程学院,贵阳 550025
- 折叠
摘要
在三维点云语义分割任务中,使用少量标注的点云数据进行语义分割可以节省人力标注成本,近年来得到学术界的普遍关注.传统的三维点云语义分割方法多利用完全监督的方式,这类方法往往需要耗费人力和时间去标注大量点云,而使用弱监督方式只需要对点云进行少量的标注就能达到和完全监督方法相同的目的.文章回顾和讨论了近年来三维点云弱监督语义分割的发展,从不同角度总结了弱监督语义分割的相关方法,基于这些方法,在四个公开数据集上对其结果进行了定量分析与讨论,最后总结了三维点云弱监督语义分割存在的挑战,并展望了未来的发展方向.
Abstract
In the 3D point cloud semantic segmentation task,using a small amount of labeled point cloud data for semantic segmentation can save the cost of human labeling,and has attracted widespread attention from the academic community in recent years.Traditional 3D point cloud semantic segmentation methods mostly use fully supervised methods,which often require manpower and time to label a large number of point clouds,while using weakly super-vised methods only requires a small amount of labeling on point clouds to achieve the same purpose as fully supervised methods.This paper reviews and discusses the development of weakly supervised semantic segmentation of 3D point cloud in recent years,and summarizes the related methods of weakly supervised semantic segmentation from different perspectives.Based on these methods,the results are quantitatively analyzed and discussed on four public datasets.Finally,the challenges of weakly supervised semantic segmentation of 3D point cloud are summarized,and the future development direction is prospected.
关键词
三维点云/计算机视觉/弱监督语义分割/点云数据集Key words
3D point cloud/computer vision/weakly supervised semantic segmentation/point cloud dataset引用本文复制引用
基金项目
国家重点研发计划(2020YFB1713302)
贵州省教育厅高等学校集成攻关大平台项目(黔教合KY字[2020]005)
贵州大学引进人才科研项目(贵大人基合字202174号)
贵州省教育厅青年科技人才成长项目(黔教合KY字[2022]142号)
山地农机"听音"智能诊断技术研究项目()
出版年
2024
NETL
NSTL
万方数据