Abstract
Interior layout design is closely related to people’s everyday life and is very widely demanded. As much workload of interior layout design being repetitive and categorized, automation and assistance could be applied with the help of most recent advancement of artificial intelligence. In this paper, we present an exploration work of automating interior layout design. We put forward a set of representation rules which turn interior scene photos into structuralized scene graphs. With representation rule containing both categorial and spatial information, we establish an interior scene graph dataset by annotating well-designed interior scene pictures downloaded from online photo sharing sites. Using the interior scene dataset which contains over 400 valid interior scene graphs, we train a graph generative model and further render its output as reconstructed scenes. The system could generate interior scene within short time and could potentially be applied in multiple related tasks.
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Su, X., Wu, C., Gao, W., Huang, W. (2022). Interior Layout Generation Based on Scene Graph and Graph Generation Model. In: Gero, J.S. (eds) Design Computing and Cognition’20. Springer, Cham. https://doi.org/10.1007/978-3-030-90625-2_15
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DOI: https://doi.org/10.1007/978-3-030-90625-2_15
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