Computer-generated residential building layouts

By Paul C. Merrell, Eric Schkufza, Vladlen Koltun. ACM Transactions on Graphics, 2010

386 citations recorded by the source corpus.

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Procedural Modeling Using Autoencoder Networks (2015, background): “Applications of PM in digital design span a wide variety of categories including plants [33, 27], cities [31], buildings [29], furniture arrangements [11], building layouts [28] and lighting design [36].”
Data-driven interior plan generation for residential buildings (2019, methodology): “Some techniques have been proposed for automatically generating floor plans [Hua 2016; Liu et al. 2013; Merrell et al. 2010; Wu et al. 2018].”
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation (2020, background): “een a focus of research. Wang et al. [23] plans an indoor scene as a relation graph and iteratively inserts a 3D model at each node via convolutional neural network (CNN) guided search. Merrel et al. [19] utilizes Bayesian Networks for retrieving candidate bubble diagrams, given high-level condition”
Probabilistic reasoning for assembly-based 3D modeling (2011, methodology): “Our use of probabilistic graphical models is influenced by the work of Merrell et al. [2010], who generate residential building layouts using a Bayesian network trained on architectural programs.”
Interactive furniture layout using interior design guidelines (2011, methodology): “A recent application of stochastic optimization to architectural layout reported running times of multiple minutes [Merrell et al. 2010].”