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data-driven Suggestions for Creativity Support in 3d modeling

ACM TRANSACTIONS ON GRAPHICS 2010年第6期29卷 1-10页

作者： Chaudhuri, Siddhartha Koltun, Vladlen Stanford Univ Stanford CA 94305 USA

We introduce data-driven suggestions for 3d modeling. data-driven suggestions support open-ended stages in the 3d modeling process, when the appearance of the desired model is ill-defined and the artist can benefit from customized examples that stimulate creativity. Our approach computes and presents components that can be added to the artist's current shape. We describe shape retrieval and shape correspondence techniques that support the generation of data-driven suggestions, and report preliminary experiments with a tool for creative prototyping of 3d models.

关键词： data-driven 3d modeling creativity support shape analysis interaction

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A Probabilistic Model for Component-Based Shape Synthesis

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ACM TRANSACTIONS ON GRAPHICS 2012年第4期31卷 1-11页

作者： Kalogerakis, Evangelos Chaudhuri, Siddhartha Koller, daphne Koltun, Vladlen Stanford Univ Stanford CA 94305 USA

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.

关键词： shape synthesis shape structure probabilistic graphical models machine learning data-driven 3d modeling

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Probabilistic Reasoning for Assembly-Based 3d modeling

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ACM TRANSACTIONS ON GRAPHICS 2011年第4期30卷 1-10页

作者： Chaudhuri, Siddhartha Kalogerakis, Evangelos Guibas, Leonidas Koltun, Vladlen Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781450309431

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.

关键词： data-driven 3d modeling probabilistic reasoning probabilistic graphical models

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Computer-Generated Residential Building Layouts

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ACM TRANSACTIONS ON GRAPHICS 2010年第6期29卷 1-12页

作者： Merrell, Paul Schkufza, Eric Koltun, Vladlen Stanford Univ Stanford CA 94305 USA

We present a method for automated generation of building layouts for computer graphics applications. Our approach is motivated by the layout design process developed in architecture. Given a set of high-level requirements, an architectural program is synthesized using a Bayesian network trained on real-world data. The architectural program is realized in a set of floor plans, obtained through stochastic optimization. The floor plans are used to construct a complete three-dimensional building with internal structure. We demonstrate a variety of computer-generated buildings produced by the presented approach.

关键词： procedural modeling architectural modeling computer-aided architectural design spatial allocation data-driven 3d modeling

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Probabilistic reasoning for assembly-based 3d modeling 11

Probabilistic reasoning for assembly-based 3D modeling

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ACM SIGGRAPH 2011 papers

作者： Siddhartha Chaudhuri Evangelos Kalogerakis Leonidas Guibas Vladlen Koltun Stanford University

ISBN: (纸本)9781450309431

关键词： probabilistic graphical models probabilistic reasoning data-driven 3d modeling

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