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Component‐wise Controllers for Structure‐Preserving Shape Manipulation
Author(s) -
Zheng Youyi,
Fu Hongbo,
CohenOr Daniel,
Au Oscar KinChung,
Tai ChiewLan
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01880.x
Subject(s) - computer science , component (thermodynamics) , focus (optics) , range (aeronautics) , interface (matter) , degrees of freedom (physics and chemistry) , parallelism (grammar) , theoretical computer science , artificial intelligence , parallel computing , materials science , physics , bubble , quantum mechanics , maximum bubble pressure method , optics , composite material , thermodynamics
Recent shape editing techniques, especially for man‐made models, have gradually shifted focus from maintaining local, low‐level geometric features to preserving structural, high‐level characteristics like symmetry and parallelism. Such new editing goals typically require a pre‐processing shape analysis step to enable subsequent shape editing. Observing that most editing of shapes involves manipulating their constituent components, we introduce component‐wise controllers that are adapted to the component characteristics inferred from shape analysis. The controllers capture the natural degrees of freedom of individual components and thus provide an intuitive user interface for editing. A typical model usually results in a moderate number of controllers, allowing easy establishment of semantic relations among them by automatic shape analysis supplemented with user interaction. We propose a component‐wise propagation algorithm to automatically preserve the established inter‐relations while maintaining the defining characteristics of individual controllers and respecting the user‐specified modeling constraints. We extend these ideas to a hierarchical setup, allowing the user to adjust the tool complexity with respect to the desired modeling complexity. We demonstrate the effectiveness of our technique on a wide range of man‐made models with structural features, often containing multiple connected pieces.