Handle URI:
http://hdl.handle.net/10754/599406
Title:
Probabilistic reasoning for assembly-based 3D modeling
Authors:
Chaudhuri, Siddhartha; Kalogerakis, Evangelos; Guibas, Leonidas; Koltun, Vladlen
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. © 2011 ACM.
Citation:
Chaudhuri S, Kalogerakis E, Guibas L, Koltun V (2011) Probabilistic reasoning for assembly-based 3D modeling. ACM SIGGRAPH 2011 papers on - SIGGRAPH ’11. Available: http://dx.doi.org/10.1145/1964921.1964930.
Publisher:
Association for Computing Machinery (ACM)
Journal:
ACM SIGGRAPH 2011 papers on - SIGGRAPH '11
Issue Date:
2011
DOI:
10.1145/1964921.1964930
Type:
Conference Paper
Sponsors:
We are grateful to Aaron Hertzmann, Sergey Levine, Jonathan Laserson, Suchi Saria, and Philipp Krahenbuhl for their comments on this paper, and to Daphne Koller for helpful discussions. Chris Platz and Hadidjah Chamberlin assisted in the preparation of figures and the supplementary video. Niels Joubert narrated the video. This work was supported in part by NSF grants SES-0835601, CCF-0641402, and FODAVA-0808515, and by KAUST Global Collaborative Research.
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Full metadata record

DC FieldValue Language
dc.contributor.authorChaudhuri, Siddharthaen
dc.contributor.authorKalogerakis, Evangelosen
dc.contributor.authorGuibas, Leonidasen
dc.contributor.authorKoltun, Vladlenen
dc.date.accessioned2016-02-28T05:50:32Zen
dc.date.available2016-02-28T05:50:32Zen
dc.date.issued2011en
dc.identifier.citationChaudhuri S, Kalogerakis E, Guibas L, Koltun V (2011) Probabilistic reasoning for assembly-based 3D modeling. ACM SIGGRAPH 2011 papers on - SIGGRAPH ’11. Available: http://dx.doi.org/10.1145/1964921.1964930.en
dc.identifier.doi10.1145/1964921.1964930en
dc.identifier.urihttp://hdl.handle.net/10754/599406en
dc.description.abstractAssembly-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. © 2011 ACM.en
dc.description.sponsorshipWe are grateful to Aaron Hertzmann, Sergey Levine, Jonathan Laserson, Suchi Saria, and Philipp Krahenbuhl for their comments on this paper, and to Daphne Koller for helpful discussions. Chris Platz and Hadidjah Chamberlin assisted in the preparation of figures and the supplementary video. Niels Joubert narrated the video. This work was supported in part by NSF grants SES-0835601, CCF-0641402, and FODAVA-0808515, and by KAUST Global Collaborative Research.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectData-driven 3D modelingen
dc.subjectProbabilistic graphical modelsen
dc.subjectProbabilistic reasoningen
dc.titleProbabilistic reasoning for assembly-based 3D modelingen
dc.typeConference Paperen
dc.identifier.journalACM SIGGRAPH 2011 papers on - SIGGRAPH '11en
dc.contributor.institutionStanford University, Palo Alto, United Statesen
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