Siddhartha Chaudhuri, Evangelos Kalogerakis, Leonidas Guibas, and Vladlen Koltun. 2011. Probabilistic reasoning for assembly-based 3D modeling. ACM Trans. Graph. 30, 4, Article 35 (August 2011), 10 pages

Abstract:

Assembly-based modeling is a promising approach to broadening the accessibility of 3D modeling. In assembly-based modeling, new models are assembled from shape components extracted from a database. A key challenge in assembly-based modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components. <p></p> <b><a href="http://graphics.stanford.edu/~sidch/projects/assembly/">Project web page</a></b>

Bibtex: 

@article{ckgk-pram-11,
 author = {Chaudhuri, Siddhartha and Kalogerakis, Evangelos and Guibas, Leonidas and Koltun, Vladlen},
 title = {Probabilistic reasoning for assembly-based 3D modeling},
 journal = {ACM Trans. Graph.},
 issue_date = {July 2011},
 volume = {30},
 issue = {4},
 month = {August},
 year = {2011},
 issn = {0730-0301},
 pages = {35:1--35:10},
 articleno = {35},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2010324.1964930},
 doi = {http://doi.acm.org/10.1145/2010324.1964930},
 acmid = {1964930},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {data-driven 3D modeling, probabilistic graphical models, probabilistic reasoning},
}