A probabilistic model for component-based shape synthesis

Evangelos Kalogerakis; Siddhartha Chaudhuri; Daphne Koller; Vladlen Koltun

doi:10.1145/2185520.2185551

A probabilistic model for component-based shape synthesis

Evangelos Kalogerakis, Siddhartha Chaudhuri, Daphne Koller, Vladlen Koltun

Research output: Contribution to journal › Article › peer-review

302 Scopus citations

Abstract

We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.

Original language	English (US)
Pages (from-to)	1-11
Number of pages	11
Journal	ACM Transactions on Graphics
Volume	31
Issue number	4
DOIs	https://doi.org/10.1145/2185520.2185551 https://doi.org/10.1145/2185520.2335406
State	Published - Jul 1 2012
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We are grateful to Aaron Hertzmann, Sergey Levine, and Philipp Krahenbuhl for their comments on this paper, and to Tom Funkhouser for helpful discussions. This research was conducted in conjunction with the Intel Science and Technology Center for Visual Computing, and was supported in part by KAUST Global Collaborative Research and by NSF grants SES-0835601 and CCF-0641402.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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title = "A probabilistic model for component-based shape synthesis",

abstract = "We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. {\textcopyright} 2012 ACM 0730-0301/2012/08-ART55.",

author = "Evangelos Kalogerakis and Siddhartha Chaudhuri and Daphne Koller and Vladlen Koltun",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: We are grateful to Aaron Hertzmann, Sergey Levine, and Philipp Krahenbuhl for their comments on this paper, and to Tom Funkhouser for helpful discussions. This research was conducted in conjunction with the Intel Science and Technology Center for Visual Computing, and was supported in part by KAUST Global Collaborative Research and by NSF grants SES-0835601 and CCF-0641402. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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AU - Chaudhuri, Siddhartha

AU - Koller, Daphne

AU - Koltun, Vladlen

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: We are grateful to Aaron Hertzmann, Sergey Levine, and Philipp Krahenbuhl for their comments on this paper, and to Tom Funkhouser for helpful discussions. This research was conducted in conjunction with the Intel Science and Technology Center for Visual Computing, and was supported in part by KAUST Global Collaborative Research and by NSF grants SES-0835601 and CCF-0641402. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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N2 - We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.

AB - We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.

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JO - ACM Transactions on Graphics

JF - ACM Transactions on Graphics

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A probabilistic model for component-based shape synthesis

Abstract

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