RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images

doi:10.1007/s11390-023-3117-x

	RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images
	Wang, Teng 1,2; Meng, Wei-Liang1,2 ; Lu, Zheng-Da 2; Guo, Jian-Wei1,2 ; Xiao, Jun 2; Zhang, Xiao-Peng1,2
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
	2023-06-01
卷号	38 期号:3 页码:526-539
通讯作者	Guo, Jian-Wei(jianwei.guo@nlpr.ia.ac.cn) ; Xiao, Jun(xiaojun@ucas.ac.cn)
摘要	The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic materials can be significantly streamlined. In this paper, we present a novel learning framework for automatically parsing floor plan images. Our key insight is that the room type text is very common and crucial in floor plan images as it identifies the important semantic information of the corresponding room. However, this clue is rarely considered in previous learning-based methods. In contrast, we propose the Row and Column network (RC-Net) for recognizing floor plan elements by integrating the text feature. Specifically, we add the text feature branch in the network to extract text features corresponding to the room type for the guidance of room type predictions. More importantly, we formulate the Row and Column constraint module (RC constraint module) to share and constrain features across the entire row and column of the feature maps to ensure that only one type is predicted in each room as much as possible, making the segmentation boundaries between different rooms more regular and cleaner. Extensive experiments on three benchmark datasets validate that our framework substantially outperforms other state-of-the-art approaches in terms of the metrics of FWIoU, mACC and mIoU.
关键词	floor plan understanding text feature Row and Column (RC) constraint module Row and Column network (RC-Net)
DOI	10.1007/s11390-023-3117-x
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:001089311400005
出版者	SPRINGER SINGAPORE PTE LTD
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54488
专题	多模态人工智能系统全国重点实验室
通讯作者	Guo, Jian-Wei; Xiao, Jun
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Teng,Meng, Wei-Liang,Lu, Zheng-Da,et al. RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2023,38(3):526-539.
APA	Wang, Teng,Meng, Wei-Liang,Lu, Zheng-Da,Guo, Jian-Wei,Xiao, Jun,&Zhang, Xiao-Peng.(2023).RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,38(3),526-539.
MLA	Wang, Teng,et al."RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38.3(2023):526-539.