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    FAME: 3D Shape Generation via Functionality-Aware Model Evolution

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    Type
    Article
    Authors
    Guan, Yanran
    Liu, Han cc
    Liu, Kun
    Yin, Kangxue
    Hu, Ruizhen
    van Kaick, Oliver
    Zhang, Yan
    Yumer, Ersin
    Carr, Nathan
    Mech, Radomir
    Zhang, Hao
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-10-12
    Online Publication Date
    2020-10-12
    Print Publication Date
    2020
    Submitted Date
    2019-05-22
    Permanent link to this record
    http://hdl.handle.net/10754/665541
    
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    Abstract
    We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D objects for data augmentation, constrained modeling, as well as open-ended exploration to possibly inspire new designs. Starting with an initial population of 3D objects belonging to one or more functional categories, we evolve the shapes through part re-combination to produce generations of hybrids or crossbreeds between parents from the heterogeneous shape collection. Evolutionary selection of offsprings is guided both by a functional plausibility score derived from functionality analysis of shapes in the initial population and user preference, as in a design gallery. Since cross-category hybridization may result in offsprings not belonging to any of the known functional categories, we develop a means for functionality partial matching to evaluate functional plausibility on partial shapes. We show a variety of plausible hybrid shapes generated by our functionality-aware model evolution, which can complement existing datasets as training data and boost the performance of contemporary data-driven segmentation schemes, especially in challenging cases.
    Citation
    Guan, Y., Liu, H., Liu, K., Yin, K., Hu, R., van Kaick, O., … Zhang, H. (2021). FAME: 3D Shape Generation via Functionality-Aware Model Evolution. IEEE Transactions on Visualization and Computer Graphics, 1–1. doi:10.1109/tvcg.2020.3029759
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Visualization and Computer Graphics
    DOI
    10.1109/TVCG.2020.3029759
    arXiv
    2005.04464
    Additional Links
    https://ieeexplore.ieee.org/document/9220814/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9220814
    http://arxiv.org/pdf/2005.04464
    ae974a485f413a2113503eed53cd6c53
    10.1109/TVCG.2020.3029759
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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