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

doi:10.1007/s11390-023-3117-x

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images
	Wang, Teng 1,2; Meng, Wei-Liang 1,2; Lu, Zheng-Da 1; Guo, Jian-Wei 1,2; Xiao, Jun 1; Zhang, Xiao-Peng 1,2
刊名	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
	2023-06-01
卷号	38 期号:3 页码:526-539
关键词	floor plan understanding text feature Row and Column (RC) constraint module Row and Column network (RC-Net)
ISSN号	1000-9000
DOI	10.1007/s11390-023-3117-x
通讯作者	Guo, Jian-Wei(jianwei.guo@nlpr.ia.ac.cn) ; Xiao, Jun(xiaojun@ucas.ac.cn)
英文摘要	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.
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER SINGAPORE PTE LTD
WOS记录号	WOS:001089311400005
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54487]
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
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.