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- W2580533918 abstract "Despite the increasing capabilities of computers to master sophisticated human-like tasksand the recent explosive new wave of Machine Learning-based methods, the interiordesign field still remains a hard-to-master area, without robust, mature models that couldcompete with the expertise of humans in the field. This is an exciting new are for ArtificialIntelligence in general, still in its very early stages of development, both in terms of modelsperformance and in terms of specialized data availability. Most current applications of thistype of models remain only in the area of virtual reality. Veering away from this trend, thecurrent thesis proposes an end-to-end proof of concept for applying Machine Learningtechniques to realistically asses the quality of professional and realistic room furniturelayouts. We do so by proposing a learning-based scoring function comprising variousinterior design guidelines, ergonomics and plain common sense metrics. We furtherpropose a stochastic optimization proof of concept based on Simulated Annealingtechniques, aiming to generate new plausible and pleasant furniture layouts that obey thestrict regulations of interior design. This proof of concept represents a first step towardsthe final goal of developing a software tool that would eventually demonstrate that realworld, furniture layouts of professional quality can be obtained in an at least semiautomaticmanner, using an energy function that analytically represents, as cost terms,various furniture functional and style interdependencies, common practices in relativefurniture positioning in a room and other ergonomic factors that contribute to obtain apleasant, livable room. Using machine learning to adapt the ranking function parametersacross various types of rooms and sophisticated furniture objects, the method is supposedto scale in modeling complex interior design know-hows, hard to be modeledmathematically or learned directly by a purely data-oriented model." @default.
- W2580533918 created "2017-02-03" @default.
- W2580533918 creator A5008564661 @default.
- W2580533918 date "2016-07-06" @default.
- W2580533918 modified "2023-09-23" @default.
- W2580533918 title "A stochastic approach for automatic layout synthesis in interior design, using a learningbased scoring function" @default.
- W2580533918 hasPublicationYear "2016" @default.
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