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.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.