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• W2620975421 abstract "Increasing numbers of 3D models provide a greatopportunity for data-driven shape modeling, analysis andsynthesis. The most critical core technique is to aggregateinformation from model collections to improve reasoningtheir properties and relationships. In this paper, we proposea probabilistic model for traditional Chinese architectures, which encodes the semantic type and hierarchicalrelationships for their basic components. Firstly aprobabilistic hierarchical graph is designed to represent thetypical component structure of Chinese ancient buildings. Secondly, a Bayesian Network is trained from a collection of3D models with consistent labels. Finally, the BayesianNetwork with structure and parameters learned from datacan be used to synthesis and recommend components inapplications. Experimental results show the effective of theproposed method." @default.
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• W2620975421 date "2016-09-01" @default.
• W2620975421 modified "2023-09-24" @default.
• W2620975421 title "A Probabilistic Model for Traditional Chinese Architecture" @default.
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• W2620975421 doi "https://doi.org/10.1109/icvrv.2016.75" @default.
• W2620975421 hasPublicationYear "2016" @default.
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