Generating Topological Structure of Floorplans from Room Attributes

• URL: http://arxiv.org/abs/2204.12338v1
• Date: Tue, 26 Apr 2022 14:24:58 GMT
• Title: Generating Topological Structure of Floorplans from Room Attributes
• Authors: Yin Yu, Hutchcroft Will, Khosravan Naji, Boyadzhiev Ivaylo, Fu Yun, Kang Sing Bing
• Abstract summary: We propose to extract topological information from room attributes using 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.
• Score: 4.1715767752637145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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