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.
|Original language||English (US)|
|Number of pages||13|
|Journal||ACM Transactions on Graphics|
|State||Published - Jul 29 2016|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OCRF-2014-CRG3-62140401
Acknowledgements: This publication is based on work supported by the Office of Sponsored Research (OSR) under Award No. OCRF-2014-CRG3-62140401 and the KAUST Visual Computing Center.