Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding

IF 9.6 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Automation in Construction Pub Date : 2024-10-07 DOI:10.1016/j.autcon.2024.105799
Limao Zhang , Zeyang Wei , Zhonghua Xiao , Ankang Ji , Beibei Wu
{"title":"Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding","authors":"Limao Zhang ,&nbsp;Zeyang Wei ,&nbsp;Zhonghua Xiao ,&nbsp;Ankang Ji ,&nbsp;Beibei Wu","doi":"10.1016/j.autcon.2024.105799","DOIUrl":null,"url":null,"abstract":"<div><div>Targeted to the challenge of indoor scene understanding for intelligent devices, this paper question focuses on enhancing accuracy in semantic information extraction. A framework including a dual hierarchical attention network, transfer learning, interpretability analysis, and modeling module is applied to segment and reconstruct the indoor scene. A high-rise as-built building case is used to verify the method, the results show that: (1) the method achieves a high mIoU of 0.970 in point cloud segmentation and outperforms state-of-the-art methods, both demonstrating strong performance; (2) the method has sound feature extraction and learning ability in term of the interpretive analysis; (3) the method accelerates by 37 % than manual operations, achieving higher accuracy and efficiency. Overall, the method provides an effective solution to segment multi-class objects for indoor scene understanding and can serve as a basis for automated modeling to contribute to an accurate BIM model with great potential for practical application.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105799"},"PeriodicalIF":9.6000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524005351","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Targeted to the challenge of indoor scene understanding for intelligent devices, this paper question focuses on enhancing accuracy in semantic information extraction. A framework including a dual hierarchical attention network, transfer learning, interpretability analysis, and modeling module is applied to segment and reconstruct the indoor scene. A high-rise as-built building case is used to verify the method, the results show that: (1) the method achieves a high mIoU of 0.970 in point cloud segmentation and outperforms state-of-the-art methods, both demonstrating strong performance; (2) the method has sound feature extraction and learning ability in term of the interpretive analysis; (3) the method accelerates by 37 % than manual operations, achieving higher accuracy and efficiency. Overall, the method provides an effective solution to segment multi-class objects for indoor scene understanding and can serve as a basis for automated modeling to contribute to an accurate BIM model with great potential for practical application.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于建筑场景理解中点云语义分割的双分层注意力增强迁移学习
针对智能设备在室内场景理解方面所面临的挑战,本文的研究重点是提高语义信息提取的准确性。本文采用了一个包括双分层注意力网络、迁移学习、可解释性分析和建模模块的框架来分割和重建室内场景。通过一个高层建筑竣工案例对该方法进行了验证,结果表明(1) 该方法在点云分割方面的 mIoU 高达 0.970,优于最先进的方法,表现出强劲的性能;(2) 该方法在可解释性分析方面具有良好的特征提取和学习能力;(3) 该方法比人工操作加快了 37%,实现了更高的精度和效率。总之,该方法为室内场景理解中的多类物体分割提供了有效的解决方案,可作为自动建模的基础,有助于建立精确的 BIM 模型,具有巨大的实际应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Automation in Construction
Automation in Construction 工程技术-工程:土木
CiteScore
19.20
自引率
16.50%
发文量
563
审稿时长
8.5 months
期刊介绍: Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities. The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.
期刊最新文献
Construction safety inspection with contrastive language-image pre-training (CLIP) image captioning and attention Signs on glasses: LiDAR data voids, hotspot effect, and reflection artifacts Automated physics-based modeling of construction equipment through data fusion Automated daily report generation from construction videos using ChatGPT and computer vision Automated rule-based safety inspection and compliance checking of temporary guardrail systems in construction
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1