Knowledge Commons of Institute of Automation,CAS
RC-Net: Row and Column Net with Text Feature for Deep Parsing Floor Plan Images | |
Wang T(王腾)1,2; Meng WL(孟维亮)1,2![]() ![]() ![]() | |
发表期刊 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
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2023 | |
页码 | 526-539 |
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. |
收录类别 | SCI |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57340 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Guo JW(郭建伟) |
作者单位 | 1.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, 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 T,Meng WL,Lu ZD,et al. RC-Net: Row and Column Net with Text Feature for Deep Parsing Floor Plan Images[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2023:526-539. |
APA | Wang T,Meng WL,Lu ZD,Guo JW,Xiao J,&Zhang XP.(2023).RC-Net: Row and Column Net with Text Feature for Deep Parsing Floor Plan Images.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,526-539. |
MLA | Wang T,et al."RC-Net: Row and Column Net with Text Feature for Deep Parsing Floor Plan Images".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2023):526-539. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
RCNet.pdf(2370KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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