Data-driven floor plan understanding in rural residential buildings via deep recognition

doi:10.1016/j.ins.2021.03.032

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 三维可视计算

	Data-driven floor plan understanding in rural residential buildings via deep recognition
	Lu, Zhengda 1; Wang, Teng 1,2; Guo, Jianwei 1,2; Meng, Weiliang 1,2,3; Xiao, Jun 1; Zhang, Wei 4; Zhang, Xiaopeng 1,2
刊名	INFORMATION SCIENCES
	2021-08-01
卷号	567 页码:58-74
关键词	Floor plan understanding Rural residence Neural networks
ISSN号	0020-0255
DOI	10.1016/j.ins.2021.03.032
通讯作者	Xiao, Jun(xiaojun@ucas.ac.cn)
英文摘要	Automatic understanding of floor plan images is a key component of various applications. Due to the style diversity of rural housing design, the latest learning-based approaches cannot achieve satisfactory recognition results. In this paper, we present a new framework for parsing floor plans of rural residence that combines semantic neural networks with a post processed room segmentation. First, we take case studies from typical residential buildings in China's rural areas and provide a novel image dataset, called RuralHomeData, containing 800 rural residence floor plans with accurate man-made annotations. Based on the dataset, we propose a new deep learning-based recognition framework using a joint neural network to predict the geometric elements and text information on the floor plan simultaneously. Our insight is that walls and openings (doors and windows) are the basic elements corresponding to the room boundary that a closed 1D loop must form a certain room. Then the semantic information (e.g., the room function) of room regions can be obtained through text detection and identification. Furthermore, we use the MIQP algorithm to divide the area containing multiple room type texts into multiple room areas. Finally, the input floor plan can be transformed into a room layout graph with room attributes and adjacent relationships. The proposed algorithm has been tested on both urban and rural datasets, and the experimental results demonstrate our efficiency and robustness compared with the state-of-the-art methods. (c) 2021 Elsevier Inc. All rights reserved.
资助项目	National Key RAMP;D Program of China[2018YFD1100901] ; National Natural Science Foundation of China[61802406] ; National Natural Science Foundation of China[U2003109] ; National Natural Science Foundation of China[61761003] ; Key Research Program of Frontier Sciences CAS[QYZDYSSWSYS004] ; Strategic Priority Research Program of CAS[XDA23090304] ; Youth Innovation Promotion Association of CAS[Y201935] ; Fundamental Research Funds for the Central Universities
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000659875500004
资助机构	National Key RAMP;D Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Research Program of CAS ; Youth Innovation Promotion Association of CAS ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45349]
专题	模式识别国家重点实验室_三维可视计算
通讯作者	Xiao, Jun
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China 4.China Architecture Design & Res Grp, Beijing 100044, Peoples R China
推荐引用方式 GB/T 7714	Lu, Zhengda,Wang, Teng,Guo, Jianwei,et al. Data-driven floor plan understanding in rural residential buildings via deep recognition[J]. INFORMATION SCIENCES,2021,567:58-74.
APA	Lu, Zhengda.,Wang, Teng.,Guo, Jianwei.,Meng, Weiliang.,Xiao, Jun.,...&Zhang, Xiaopeng.(2021).Data-driven floor plan understanding in rural residential buildings via deep recognition.INFORMATION SCIENCES,567,58-74.
MLA	Lu, Zhengda,et al."Data-driven floor plan understanding in rural residential buildings via deep recognition".INFORMATION SCIENCES 567(2021):58-74.