Generating Topological Structure of Floorplans from Room Attributes

TL;DR

This paper introduces ITL, an iterative method that learns the topological structure of indoor floorplans from room attributes, improving the accuracy of layout topology prediction and floorplan generation.

Contribution

It proposes a novel iterative graph learning approach that adaptively predicts room relations, enhancing topological understanding of indoor spaces from attributes.

Findings

ITL outperforms baseline methods in topology prediction accuracy.

Qualitative results show improved floorplan layouts.

Quantitative metrics confirm the effectiveness of the learned topologies.

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.