论文标题
从房间属性生成平面图的拓扑结构
Generating Topological Structure of Floorplans from Room Attributes
论文作者
论文摘要
室内空间的分析需要拓扑信息。在本文中，我们建议使用所谓的迭代和自适应图形拓扑学习（ITL）从房间属性中提取拓扑信息。 ITL逐渐预测房间之间的多个关系。在每次迭代中，它都会改善节点嵌入，这反过来促进了更好的拓扑图结构的生成。节点嵌入和拓扑图结构的迭代改进的概念与\ cite {Chen2020术语}具有相同的精神。但是，虽然\ cite {chen2020 interative}基于节点相似性计算邻接矩阵，但我们使用关系解码器来学习图形指标来提取房间相关性。使用新挑战的室内数据集的实验验证了我们提出的方法。布局拓扑预测和平面图生成应用的定性和定量评估也证明了ITL的有效性。
Analysis of indoor spaces requires topological information. In this paper, we propose to extract topological information from room attributes using what we call Iterative and adaptive graph Topology Learning (ITL). ITL progressively predicts multiple relations between rooms; at each iteration, it improves node embeddings, which in turn facilitates generation of a better topological graph structure. This notion of iterative improvement of node embeddings and topological graph structure is in the same spirit as \cite{chen2020iterative}. However, while \cite{chen2020iterative} computes the adjacency matrix based on node similarity, we learn the graph metric using a relational decoder to extract room correlations. Experiments using a new challenging indoor dataset validate our proposed method. Qualitative and quantitative evaluation for layout topology prediction and floorplan generation applications also demonstrate the effectiveness of ITL.
