A generalized semantic representation for procedural generation of rooms

Conference Paper (2019)
Author(s)

J.T. Balint (TU Delft - Computer Graphics and Visualisation)

Rafael Bidarra (TU Delft - Computer Graphics and Visualisation)

Research Group
Computer Graphics and Visualisation
Copyright
© 2019 J.T. Balint, Rafael Bidarra
DOI related publication
https://doi.org/10.1145/3337722.3341848
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 J.T. Balint, Rafael Bidarra
Research Group
Computer Graphics and Visualisation
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
1-8
ISBN (print)
978-1-4503-7217-6
Reuse Rights

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Abstract

Procedural generation of rooms aims to create virtual environments that mimic common patterns found in real-world indoor locations, like offices or bedrooms. Graph-based models (e.g. factor graphs or Bayesian networks) have often been used to represent typical location's objects and their occurrence likelihood (nodes), as well as their inter-relationships (edges). Previous methods have struggled to represent object semantics in their graph nodes; specifically, they fail to fully and effectively support notions as abstractions (e.g. generic seat instead of chair) and replication (e.g. cups instead of cup). We propose a generalized representation and use for object semantics that overcomes the above limitations of graph-based models in the procedural generation of rooms. This node representation handles semantics as attributes, and clearly distinguishes the contribution of the attributes on the node from the potential effects of the node on the whole graph. We illustrate the additional expressive power of the resulting graph-based model for room generation, and show that it subsumes previous models as particular cases.

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