WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
Machine Learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Metropolis procedural modeling
ACM Transactions on Graphics (TOG)
Make it home: automatic optimization of furniture arrangement
ACM SIGGRAPH 2011 papers
Interactive furniture layout using interior design guidelines
ACM SIGGRAPH 2011 papers
Extending factor graphs so as to unify directed and undirected graphical models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-based synthesis of 3D object arrangements
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Learning design patterns with bayesian grammar induction
Proceedings of the 25th annual ACM symposium on User interface software and technology
The principles and practice of probabilistic programming
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Improving markov chain monte carlo estimation with agent-based models
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Generating and exploring good building layouts
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Probabilistic color-by-numbers: suggesting pattern colorizations using factor graphs
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Reshuffle-based interior scene synthesis
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
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We present a novel Markov chain Monte Carlo (MCMC) algorithm that generates samples from transdimensional distributions encoding complex constraints. We use factor graphs, a type of graphical model, to encode constraints as factors. Our proposed MCMC method, called locally annealed reversible jump MCMC, exploits knowledge of how dimension changes affect the structure of the factor graph. We employ a sequence of annealed distributions during the sampling process, allowing us to explore the state space across different dimensionalities more freely. This approach is motivated by the application of layout synthesis where relationships between objects are characterized as constraints. In particular, our method addresses the challenge of synthesizing open world layouts where the number of objects are not fixed and optimal configurations for different numbers of objects may be drastically different. We demonstrate the applicability of our approach on two open world layout synthesis problems: coffee shops and golf courses.