mlsanityData to 14 Oct 2026

Generating Topological Structure of Floorplans from Room Attributes

arXiv 2204.12338 ↗First posted 26 Apr 2022cs.CVcited by 1

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Abstract (arXiv)

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 citechen2020iterative. However, while citechen2020iterative 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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

24 of 43 references.

References · 24

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12 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexescited by 14