Abstract
We propose a new framework to model the exterior of residential buildings. The main goal of our work is to design a model that can be learned from data that is observable from the outside of a building and that can be trained with widely available data such as aerial images and street-view images. First, we propose a parametric model to describe the exterior of a building (with a varying number of parameters) and propose a set of attributes as a building representation with fixed dimensionality. Second, we propose a hierarchical graphical model with hidden variables to encode the relationships between building attributes and learn both the structure and parameters of the model from the database. Third, we propose optimization algorithms to generate three-dimensional models based on building attributes sampled from the graphical model. Finally, we demonstrate our framework by synthesizing new building models and completing partially observed building models from photographs.
Supplemental Material
Available for Download
Supplemental movie, appendix, image and software files for, A Probabilistic Model for Exteriors of Residential Buildings
- Oswin Aichholzer, Franz Aurenhammer, David Alberts, and Bernd Gärtner. 1996. A Novel Type of Skeleton for Polygons. Springer, New York, NY.Google Scholar
- Melinos Averkiou, Vladimir Kim, Youyi Zheng, and Niloy J. Mitra. 2014. ShapeSynth: Parameterizing model collections for coupled shape exploration and synthesis. Computer Graphics Forum 33, 2, 125--134. Google ScholarDigital Library
- Fan Bao, Michael Schwarz, and Peter Wonka. 2013a. Procedural facade variations from a single layout. ACM Transactions on Graphics 32, 1, 8:1--8:13. Google ScholarDigital Library
- Fan Bao, Dong-Ming Yan, Niloy J. Mitra, and Peter Wonka. 2013b. Generating and exploring good building layouts. ACM Transactions on Graphics 32, 4, 122:1--122:10. Google ScholarDigital Library
- Martin Bokeloh, Michael Wand, and Hans-Peter Seidel. 2010. A connection between partial symmetry and inverse procedural modeling. ACM Transactions on Graphics 29, 4, 104:1--104:10. Google ScholarDigital Library
- Neill D. F. Campbell and Jan Kautz. 2014. Learning a manifold of fonts. ACM Transactions on Graphics 33, 4, 91:1--91:11. Google ScholarDigital Library
- Siddhartha Chaudhuri, Evangelos Kalogerakis, Leonidas Guibas, and Vladlen Koltun. 2011. Probabilistic reasoning for assembly-based 3D modeling. ACM Transactions on Graphics 30, 4, 35:1--35:10. Google ScholarDigital Library
- Siddhartha Chaudhuri, Evangelos Kalogerakis, Stephen Giguere, and Thomas Funkhouser. 2013. AttribIt: Content creation with semantic attributes. Proc. UIST. ACM. Google ScholarDigital Library
- Peter Cheeseman and John Stutz. 1996. Bayesian classification (AutoClass): Theory and results. In Advances in Knowledge Discovery and Data Mining. 153--180. Google ScholarDigital Library
- Lubin Fan, Przemyslaw Musialski, Ligang Liu, and Peter Wonka. 2014. Structure completion for facade layouts. ACM Transactions on Graphics 33, 6, 210:1--210:11. Google ScholarDigital Library
- David Heckerman. 1998. A Tutorial on Learning with Bayesian Networks. Springer, New York, NY.Google Scholar
- Evangelos Kalogerakis, Siddhartha Chaudhuri, Daphne Koller, and Vladlen Koltun. 2012. A probabilistic model for component-based shape synthesis. ACM Transactions on Graphics 31, 4, 55:1--55:11. Google ScholarDigital Library
- Vladimir G. Kim, Wilmot Li, Niloy J. Mitra, Siddhartha Chaudhuri, Stephen DiVerdi, and Thomas Funkhouser. 2013. Learning part-based templates from large collections of 3D shapes. ACM Transactions on Graphics 32, 4, 70:1--70:12. Google ScholarDigital Library
- Daphne Koller and Nir Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, MA. Google ScholarDigital Library
- Jinjie Lin, Daniel Cohen-Or, Hao Zhang, Cheng Liang, Andrei Sharf, Oliver Deussen, and Baoquan Chen. 2011. Structure-preserving retargeting of irregular 3D architecture. ACM Transactions on Graphics 30, 6, 183:1--183:10. Google ScholarDigital Library
- A. Martinovic and L. Van Gool. 2013. Bayesian grammar learning for inverse procedural modeling. In CVPR. 201--208. Google ScholarDigital Library
- Paul Merrell, Eric Schkufza, and Vladlen Koltun. 2010. Computer-generated residential building layouts. ACM Transactions on Graphics 29, 6, 181:1--181:12. Google ScholarDigital Library
- Pascal Müller, Peter Wonka, Simon Haegler, Andreas Ulmer, and Luc Van Gool. 2006. Procedural modeling of buildings. ACM Transactions on Graphics 25, 3, 614--623. Google ScholarDigital Library
- Przemyslaw Musialski, Peter Wonka, Daniel G. Aliaga, Michael Wimmer, Luc van Gool, and Werner Purgathofer. 2013. A survey of urban reconstruction. Computer Graphics Forum 32, 6, 146--177. Google ScholarDigital Library
- Jorma Rissanen. 1983. A universal prior for integers and estimation by minimum description length. Annals of Statistics, 416--431.Google ScholarCross Ref
- Michael Schwarz and Pascal Müller. 2015. Advanced procedural modeling of architecture. ACM Transactions on Graphics 34, 4, 107:1--107:12. Google ScholarDigital Library
- Ruben M. Smelik, Tim Tutenel, Rafael Bidarra, and Bedrich Benes. 2014. A survey on procedural modelling for virtual worlds. Computer Graphics Forum 33, 6, 31--50.Google ScholarDigital Library
- O. Št’ava, B. Beneš, R. Měch, Daniel G. Aliaga, and P. Krištof. 2010. Inverse procedural modeling by automatic generation of L-systems. Computer Graphics Forum 29, 2, 665--674.Google ScholarCross Ref
- Jerry Talton, Lingfeng Yang, Ranjitha Kumar, Maxine Lim, Noah Goodman, and Radomír Měch. 2012. Learning design patterns with Bayesian grammar induction. In Proceedings of UIST. 63--74. Google ScholarDigital Library
- Jerry O. Talton, Yu Lou, Steve Lesser, Jared Duke, Radomír Měch, and Vladlen Koltun. 2011. Metropolis procedural modeling. ACM Transactions on Graphics. 30, 2, 11:1--11:14. Google ScholarDigital Library
- Carlos A. Vanegas, Daniel G. Aliaga, Peter Wonka, Pascal Müller, Paul Waddell, and Benjamin Watson. 2010. Modelling the appearance and behaviour of urban spaces. Computer Graphics Forum 29, 1, 25--42.Google ScholarCross Ref
- Peter Wonka, Michael Wimmer, François X. Sillion, and William Ribarsky. 2003. Instant architecture. ACM Transactions on Graphics 22, 3, 669--677. Google ScholarDigital Library
- Fuzhang Wu, Dong-Ming Yan, Weiming Dong, Xiaopeng Zhang, and Peter Wonka. 2014. Inverse procedural modeling of facade layouts. ACM Transactions on Graphics 33, 4, 121:1--121:10. Google ScholarDigital Library
- Yong-Liang Yang, Yi-Jun Yang, Helmut Pottmann, and Niloy J. Mitra. 2011. Shape space exploration of constrained meshes. ACM Transactions on Graphics 30, 6, 124:1--124:12. Google ScholarDigital Library
- Hao Zhang, Kai Xu, Wei Jiang, Jinjie Lin, Daniel Cohen-Or, and Baoquan Chen. 2013. Layered analysis of irregular facades via symmetry maximization. ACM Transactions on Graphics 32, 4, 121:1--121:13. Google ScholarDigital Library
Index Terms
- A Probabilistic Model for Exteriors of Residential Buildings
Recommendations
A Structural Approach to the Model Generalization of an Urban Street Network
This paper proposes a novel generalization model for selecting characteristic streets in an urban street network. This model retains the central structure of a street network. It relies on a structural representation of a street network using graph ...
Complete residential urban area reconstruction from dense aerial LiDAR point clouds
We present an automatic system to reconstruct 3D urban models for residential areas from aerial LiDAR scans. The key difference between downtown area modeling and residential area modeling is that the latter usually contains rich vegetation. Thus, we ...
Modeling the citizens' settlement in residential buildings
AbstractThe article proposes a method of estimating the number and structure of the population living in each dwelling house of the city. This information is used in solving a number of applied problems of urban analysis and urban planning, including the ...
Comments