research-article

A probabilistic model for component-based shape synthesis

Authors:

Evangelos Kalogerakis,

Siddhartha Chaudhuri,

Vladlen KoltunAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 31, Issue 4

Article No.: 55, Pages 1 - 11

https://doi.org/10.1145/2185520.2185551

Published: 01 July 2012 Publication History

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.

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Index Terms

A probabilistic model for component-based shape synthesis
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 31, Issue 4

July 2012

935 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2185520

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Copyright © 2012 ACM.

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Publication History

Published: 01 July 2012

Published in TOG Volume 31, Issue 4

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