Paper Info
Title
Data-driven floor plan understanding in rural residential buildings via deep recognition
Abstract
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.
Year
DOI
Venue
2021
10.1016/j.ins.2021.03.032
Information Sciences
Keywords
DocType
Volume
Floor plan understanding,Rural residence,Neural networks
Journal
567
ISSN
Citations 
PageRank 
0020-0255
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Zhengda Lu100.34
Teng Wang200.34
Jianwei Guo32711.29
Weiliang Meng422.38
Jun Xiao51911.39
Wei Zhang600.34
Xiaopeng Zhang737236.34