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)


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.


Automatic layout Probabilistic model Constrained optimization 



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].


  1. 1.
    Sketchup (2017).
  2. 2.
  3. 3.
  4. 4.
    Fisher, M., Hanrahan, P.: Context-based search for 3d models. ACM Transactions on Graphics (TOG) 29(4), 182 (2010)Google Scholar
  5. 5.
    Savva, M., Chang, A.X., Agrawala, M.: Scenesuggest: Context-driven 3D scene design. arXiv preprint arXiv:1703.00061 (2017)
  6. 6.
    Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., Hanrahan, P.: Example-based synthesis of 3D object arrangements. ACM Trans. Graphics (TOG) 30(4), 135 (2012)Google Scholar
  7. 7.
    Yu, L.F., Yeung, S.K., Terzopoulos, D.: The clutterpalette: an interactive tool for detailing indoor scenes. IEEE Trans. Visual Comput. Graphics 22(2), 1138–1148 (2016)CrossRefGoogle Scholar
  8. 8.
    Xu, K., Chen, K., Fu, H., Sun, W.L., Hu, S.M.: Sketch2Scene: Sketch-based co-retrieval and co-placement of 3D models. ACM Trans. Graphics (TOG) 32(4), 123 (2013)CrossRefGoogle Scholar
  9. 9.
    Chang, A.X., Eric, M., Savva, M., Manning, C.D.: SceneSeer: 3D scene design with natural language. arXiv preprint arXiv:1703.00050 (2017)
  10. 10.
    Merrell, P., Schkufza, E., Li, Z., Agrawala, M., Koltun, V.: Interactive furniture layout using interior design guidelines. ACM Trans. Graphics (TOG) 30(4), 87 (2011)CrossRefGoogle Scholar
  11. 11.
    Yu, L.F., Yeung, S.K., Tang, C.K., Terzopoulos, D., Chan, T.F., Osher, S.J.: Make it home: automatic optimization of furniture arrangement. ACM Trans. Graphics (TOG) 30(4), 86 (2011)CrossRefGoogle Scholar
  12. 12.
    Yeh, Y.T., Yang, L., Watson, M., Goodman, N.D., Hanrahan, P.: Synthesizing open worlds with constraints using locally annealed reversible jump mcmc. ACM Trans. Graphics (TOG) 31(4), 56 (2012)CrossRefGoogle Scholar
  13. 13.
    Xiao, J., Owens, A., Torralba, A.: SUN3D: A database of big spaces reconstructed using SfM and object labels. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1625–1632 (2013)Google Scholar
  14. 14.
    The sims 4 (2017).
  15. 15.
    Papadimitriou, C.H., Tamaki, H., Raghavan, P., Vempala, S.: Latent semantic indexing: a probabilistic analysis. In: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 159–168. ACM (1998)Google Scholar
  16. 16.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  17. 17.
    Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 248–256. Association for Computational Linguistics (2009)Google Scholar
  18. 18.
    Liu, J.S.: The collapsed gibbs sampler in Bayesian computations with applications to a gene regulation problem. J. Am. Stat. Assoc. 89(427), 958–966 (1994)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Fruchterman, T.M., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Experience 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  20. 20.
    Fisher, M., Savva, M., Hanrahan, P.: Characterizing structural relationships in scenes using graph kernels. ACM Trans. Graphics (TOG) 30(4), 34 (2011)CrossRefGoogle Scholar
  21. 21.
    Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1–27 (1974)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Liu, J.S., Liang, F., Wong, W.H.: The multiple-try method and local optimization in metropolis sampling. J. Am. Stat. Assoc. 95(449), 121–134 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Griffiths, D., Tenenbaum, M.: Hierarchical topic models and the nested chinese restaurant process. Adv. Neural Inform. Process. Syst. 16, 17 (2004)Google Scholar
  24. 24.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)Google Scholar

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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