Graph-based generative representation learning of semantically and behaviorally augmented floorplans

Azizi, Vahid; Usman, Muhammad; Zhou, Haifang; Faloutsos, Petros; Kapadia, Mubbasir

doi:10.1007/s00371-021-02155-w

Cited by 12 publications

(1 citation statement)

References 34 publications

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“…41 The walk length has dependency to the average of room numbers in a data set and can be set experimentally for best performance. 42 As mentioned, we have 5000 floorplans, and in total, we have 363,983 sequences. The sequences are divided into train and test set with a ratio of 80% to 20%.…”

Section: Training Detailsmentioning

confidence: 99%

The role of latent representations for design space exploration of floorplans

et al. 2022

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Floorplans often require considering numerous factors, from the layout size to cost, numeric attributes such as room sizes, and other intrinsic properties such as connectivity between visible regions. Representing these complex factors is challenging, but doing so in a representative and efficient way can enable new modes of design exploration. Existing image and graph-based approaches of floorplans’ representation often failed to consider low-level space semantics, structural features, and space utilization with respect to its future inhabitants, which are all the critical elements to analyze design layouts. We present a latent-space representation of floorplans using gated recurrent unit variational autoencoder (GRU-VAE), where floorplans are represented as attributed graphs (encoded with the abovementioned features). Two local search approaches are presented to efficiently explore the latent space for optimizing and generating new floorplans for the given environment. Semantic, structural, and visibility metrics are evaluated individually and as a combined objective for optimizations.

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Section: Training Detailsmentioning

confidence: 99%