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Learning guidelines for automatic indoor scene design

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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. We solve the placement problem efficiently by introducing a solver based on Markov Chain Monte Carlo with a specific proposal function designed for the problem.

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References

  1. Archicad (2017) http://www.graphisoft.com/archicad/

  2. Autodesk revit (2017) https://www.autodesk.com/products/revit-family/overview

  3. Belloni A, Chernozhukov V et al. (2009) On the computational complexity of mcmc-based estimators in large samples. Ann Stat 37(4):2011–2055

    Article  MathSciNet  MATH  Google Scholar 

  4. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    MATH  Google Scholar 

  5. Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 7(4):434–455

    MathSciNet  Google Scholar 

  6. Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Communications in Statistics-theory and Methods 3(1):1–27

    Article  MathSciNet  MATH  Google Scholar 

  7. Chang AX, Eric M, Savva M, Manning CD (2017) Sceneseer: 3d scene design with natural language. arXiv:1703.00050

  8. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst Hum 43(4):996–1002

    Article  Google Scholar 

  9. Fisher M, Hanrahan P (2010) Context-based search for 3d models. In: ACM transactions on graphics (TOG), vol 29. ACM, p 182

  10. Fisher M, Ritchie D, Savva M, Funkhouser T, Hanrahan P (2012) Example-based synthesis of 3d object arrangements. ACM Trans Graph 31 (6):135:1–135:11. https://doi.org/10.1145/2366145.2366154

    Article  Google Scholar 

  11. Fisher M, Savva M, Hanrahan P (2011) Characterizing structural relationships in scenes using graph kernels. In: ACM transactions on graphics (TOG), vol 30. ACM, p 34

  12. Fruchterman TM, Reingold EM (1991) Graph drawing by force-directed placement. Software: Practice and Experience 21(11):1129–1164

    Google Scholar 

  13. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Statistical science pp 457–472

  14. Griffiths D, Tenenbaum M (2004) Hierarchical topic models and the nested chinese restaurant process. Adv Neural Inf Proces Syst 16:17

    Google Scholar 

  15. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(suppl 1):5228–5235

    Article  Google Scholar 

  16. Guo X, Lin J, Xu K, Jin X (2014) Creature grammar for creative modeling of 3d monsters. Graph Model 76(5):376–389

    Article  Google Scholar 

  17. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  18. Liu JS (2008) Monte Carlo strategies in scientific computing. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  19. Liu JS, Liang F, Wong WH (2000) The multiple-try method and local optimization in metropolis sampling. J Am Stat Assoc 95(449):121–134

    Article  MathSciNet  MATH  Google Scholar 

  20. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: Proceedings of the Thirtieth AAAI conference on artificial intelligence, pp 1266–1272. AAAI Press

  21. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: 2012 21st international conference on pattern recognition (ICPR), pp 898–901. IEEE

  22. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: recognizing complex activities from sensor data. In: Proceedings of the 24th international conference on artificial intelligence, pp 1617–1623. AAAI Press

  23. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  24. Merrell P, Schkufza E, Li Z, Agrawala M, Koltun V (2011) Interactive furniture layout using interior design guidelines. In: ACM transactions on graphics (TOG), vol. 30. ACM, p 87

  25. Murray L (2010) Distributed markov chain monte carlo. In: Proceedings of neural information processing systems workshop on learning on cores, clusters and clouds, vol 11

  26. Padmanabhan VN, Mogul JC (1996) Using predictive prefetching to improve world wide web latency. ACM SIGCOMM Computer Communication Review 26(3):22–36

    Article  Google Scholar 

  27. Papadimitriou CH, Tamaki H, Raghavan P, Vempala S (1998) Latent semantic indexing: a probabilistic analysis. In: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, ACM. pp 159–168

  28. Ramage D, Hall D, Nallapati R, Manning CD (2009) 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. Association for Computational Linguistics, pp 248–256

  29. Savva M, Chang AX, Agrawala M (2017) Scenesuggest: Context-driven 3d scene design. arXiv:1703.00061

  30. Sketchup (2017) http://www.sketchup.com

  31. The sims 4 (2017) https://www.thesims.com/

  32. Xiao J, Owens A, Torralba A (2013) 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

  33. Xu K, Chen K, Fu H, Sun WL, Hu SM (2013) Sketch2scene: sketch-based co-retrieval and co-placement of 3d models. ACM Trans Graph (TOG) 32(4):123

    Article  Google Scholar 

  34. Xu K, Zhang H, Cohen-Or D, Chen B (2012) Fit and diverse: set evolution for inspiring 3d shape galleries. ACM Trans Graph (TOG) 31(4):57

    Article  Google Scholar 

  35. Yeh YT, Yang L, Watson M, Goodman ND, Hanrahan P (2012) Synthesizing open worlds with constraints using locally annealed reversible jump mcmc. ACM Trans Graph (TOG) 31(4):56

    Article  Google Scholar 

  36. Yu LF, Yeung SK, Tang CK, Terzopoulos D, Chan TF, Osher SJ (2011) Make it home: Automatic optimization of furniture arrangement. In: ACM transactions on graphics (TOG)

  37. Yu LF, Yeung SK, Terzopoulos D (2016) The clutterpalette: an interactive tool for detailing indoor scenes. IEEE Trans Vis Comput Graph 22(2):1138–1148

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (No. 2017YFB1002604), the National Natural Science Foundation of China (No. 61772298), 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∘ [612627].

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Authors and Affiliations

  1. TNList, Tsinghua University, Beijing, China

    Yuan Liang & Song-Hai Zhang

  2. School of Computer Science, Informatics, Cardiff University, Cardiff, CF24 3AA, UK

    Ralph Robert Martin

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  1. Yuan Liang
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  2. Song-Hai Zhang
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  3. Ralph Robert Martin
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Correspondence to Yuan Liang.

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Liang, Y., Zhang, SH. & Martin, R.R. Learning guidelines for automatic indoor scene design. Multimed Tools Appl 78, 5003–5023 (2019). https://doi.org/10.1007/s11042-018-6004-7

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  • DOI: https://doi.org/10.1007/s11042-018-6004-7

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