Automatic Data-Driven Room Design Generation

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.

[1]  Pat Hanrahan,et al.  Context-based search for 3D models , 2010, SIGGRAPH 2010.

[2]  Thomas L. Griffiths,et al.  Hierarchical Topic Models and the Nested Chinese Restaurant Process , 2003, NIPS.

[3]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Andrew Owens,et al.  SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels , 2013, 2013 IEEE International Conference on Computer Vision.

[6]  Pat Hanrahan,et al.  Example-based synthesis of 3D object arrangements , 2012, ACM Trans. Graph..

[7]  Jun S. Liu,et al.  The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .

[8]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[9]  Angel X. Chang,et al.  SceneSeer: 3D Scene Design with Natural Language , 2017, ArXiv.

[10]  Maneesh Agrawala,et al.  SceneSuggest: Context-driven 3D Scene Design , 2017, ArXiv.

[11]  Chi-Keung Tang,et al.  Make it home: automatic optimization of furniture arrangement , 2011, SIGGRAPH 2011.

[12]  Pat Hanrahan,et al.  Characterizing structural relationships in scenes using graph kernels , 2011, SIGGRAPH 2011.

[13]  Ramesh Nallapati,et al.  Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora , 2009, EMNLP.

[14]  Maneesh Agrawala,et al.  Interactive furniture layout using interior design guidelines , 2011, SIGGRAPH 2011.

[15]  Santosh S. Vempala,et al.  Latent semantic indexing: a probabilistic analysis , 1998, PODS '98.

[16]  Jun S. Liu,et al.  The Multiple-Try Method and Local Optimization in Metropolis Sampling , 2000 .

[17]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[18]  Pat Hanrahan,et al.  Synthesizing open worlds with constraints using locally annealed reversible jump MCMC , 2012, ACM Trans. Graph..

[19]  Demetri Terzopoulos,et al.  The Clutterpalette: An Interactive Tool for Detailing Indoor Scenes , 2016, IEEE Transactions on Visualization and Computer Graphics.

[20]  Shi-Min Hu,et al.  Sketch2Scene: sketch-based co-retrieval and co-placement of 3D models , 2013, ACM Trans. Graph..