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Generating Topological Structure of Floorplans from Room Attributes

Published: 27 June 2022 Publication History

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 the 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 [5]. However, while [5] 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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References

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Cited By

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  • (2024)MSD: A Benchmark Dataset for Floor Plan Generation of Building ComplexesComputer Vision – ECCV 202410.1007/978-3-031-73636-0_4(60-75)Online publication date: 5-Nov-2024
  • (2024)Hierarchical Bottom-Up Procedural Modeling for Completion of Partial Virtual ScenesAdvanced Technologies, Systems, and Applications IX10.1007/978-3-031-71694-2_62(892-907)Online publication date: 1-Oct-2024
  • (2024)Artificial Intelligence for Predicting Reuse PatternsA Circular Built Environment in the Digital Age10.1007/978-3-031-39675-5_4(57-78)Online publication date: 4-Jan-2024
  • Show More Cited By

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cover image ACM Conferences
ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval
June 2022
714 pages
ISBN:9781450392389
DOI:10.1145/3512527
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 June 2022

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Author Tags

  1. floorplans
  2. graph convolutional network
  3. graph learning
  4. topological structure generation

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

View all
  • (2024)MSD: A Benchmark Dataset for Floor Plan Generation of Building ComplexesComputer Vision – ECCV 202410.1007/978-3-031-73636-0_4(60-75)Online publication date: 5-Nov-2024
  • (2024)Hierarchical Bottom-Up Procedural Modeling for Completion of Partial Virtual ScenesAdvanced Technologies, Systems, and Applications IX10.1007/978-3-031-71694-2_62(892-907)Online publication date: 1-Oct-2024
  • (2024)Artificial Intelligence for Predicting Reuse PatternsA Circular Built Environment in the Digital Age10.1007/978-3-031-39675-5_4(57-78)Online publication date: 4-Jan-2024
  • (2023)A Survey of Procedural Modelling Methods for Layout Generation of Virtual ScenesComputer Graphics Forum10.1111/cgf.1498943:1Online publication date: 13-Oct-2023

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