Deep Floor Plan Analysis for Complicated Drawings Based on Style Transfer
Deep Floor Plan Analysis for Complicated Drawings Based on Style Transfer
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DOI：
10.1061/(asce)cp.1943-5487.0000942
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发表时间：
2021-03-01
影响因子：
6.9
通讯作者：
Yu, Kiyun
中科院分区：
文献类型：
--
作者：
Kim, Seongyong;Park, Seula;Yu, Kiyun
本文提出了一种新的方法来检索室内结构从光栅图像的复杂平面图。我们提取平面图中的建筑元素，并将其处理为矢量化形式，以提供室内布局信息。与传统方法不同，该模型在识别被模糊图案（包括叠加图形和不规则符号）包围的房间和开口时具有鲁棒性。为此，我们在矢量化之前使用条件生成对抗网络将各种平面图格式集成为统一的风格。这种风格转换平面遵循统一的风格，直观地表示了房间结构，由于其简洁的表达，很容易矢量化。光栅到矢量的转换是在布局的结点单元中进行组合优化的。实验结果表明，当实现复杂的图纸，我们的模型是与现有的方法在检测和识别的房间，并提供了一个更好的分数在一对一的匹配。
This paper presents a novel approach to retrieve indoor structures from raster images of complicated floor plans. We extract the building elements in the floor plan and process them into a vectorized form to provide indoor layout information. Unlike conventional approaches, the proposed model is robust when recognizing rooms and openings surrounded by obscuring patterns, including superimposed graphics and irregular notation. To this end, we integrate various floor plan formats into a unified style using conditional generative adversarial networks prior to vectorization. This style-transferred plan that follows the unified style represents the room structure intuitively and is readily vectorized due to its concise expression. Raster-to-vector conversion is conducted with a combinatorial optimization in junction units of the layout. The experimental results demonstrate that when implemented with complex drawings, our model is comparable to existing methods in the detection and recognition of rooms and provides a much better score in one-to-one matches.