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Automatic Data-Driven Room Design Generation

  • Yuan Liang
  • Song-Hai ZhangEmail author
  • Ralph Robert Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10582)

Abstract

In this work, we address a novel and practical problem of automatically generating a room design from given room function and basic geometry, which can be described as picking appropriate objects from a given database, and placing the objects with a group of pre-defined criteria. We formulate both object selection and placement problems as probabilistic models. The object selection is first formulated as a supervised generative model, to take room function into consideration. Object placement problem is then formulated as a Bayesian model, where parameters are inferred with Maximizing a Posteriori (MAP) objective. By introducing a solver based on Markov Chain Monte Carlo (MCMC), the placement problem is solved efficiently.

Keywords

Automatic layout Probabilistic model Constrained optimization 

Notes

Acknowledgments

This work was supported by Research Grant of Beijing Higher Institution Engineering Research Center and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n\(^{\circ }\) [612627].

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuan Liang
    • 1
  • Song-Hai Zhang
    • 1
    Email author
  • Ralph Robert Martin
    • 2
  1. 1.TNListTsinghua UniversityBeijingChina
  2. 2.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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