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.

翻译：室内空间分析需要地形学信息。 在本文中, 我们提议用我们所谓的迭代和适应性图形地形学(ITL)从房间属性中提取地形学信息。 国际交易日志逐步预测各室之间的多重关系; 每次迭代时,它会改进节点嵌入,这反过来又有助于生成更好的地形图结构。 这种对节点嵌入和地形图结构进行迭代改进的概念与\cite{chen2020itementr} 有着相同的精神。 然而, 虽然\cite{chen20202020itetraty} 计算基于近似性的对称矩阵,但我们利用关系解码器来获取图表测量空间相关性。 使用新的具有挑战性的室内数据集的实验验证了我们拟议的方法。 对布局地貌预测和地貌图生成应用的定性和定量评价也证明了国际交易日志的有效性。