RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images
文献类型：期刊论文
| 作者 | Wang, Teng1,2; Meng, Wei-Liang1,2 ; Lu, Zheng-Da1; Guo, Jian-Wei1,2; Xiao, Jun1; Zhang, Xiao-Peng1,2
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| 刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
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| 出版日期 | 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 |
| WOS记录号 | WOS:001089311400005 |
| 出版者 | SPRINGER SINGAPORE PTE LTD |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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