• Login
    View Item 
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 34 (2015)
    • 34-Issue 5
    • View Item
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 34 (2015)
    • 34-Issue 5
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Analysis and Synthesis of 3D Shape Families via Deep-learned Generative Models of Surfaces

    Thumbnail
    View/Open
    v34i5pp025-038.pdf (34.69Mb)
    paper_supplementary.pdf (210.5Kb)
    user_study.pdf (270.7Kb)
    finegrainedclassification.zip (22.67Mb)
    groundtruthsegmentation_bhcp_dataset.zip (54.34Mb)
    ourresults_correspondences_segmentation.zip (78.21Mb)
    ourresults_synthesis.zip (6.260Mb)
    sourcecode.zip (180.3Kb)
    Date
    2015
    Author
    Huang, Haibin
    Kalogerakis, Evangelos
    Marlin, Benjamin
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    We present a method for joint analysis and synthesis of geometrically diverse 3D shape families. Our method first learns part-based templates such that an optimal set of fuzzy point and part correspondences is computed between the shapes of an input collection based on a probabilistic deformation model. In contrast to previous template-based approaches, the geometry and deformation parameters of our part-based templates are learned from scratch. Based on the estimated shape correspondence, our method also learns a probabilistic generative model that hierarchically captures statistical relationships of corresponding surface point positions and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical relationships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection. The generative model also yields compact shape descriptors that are used to perform fine-grained classification. Finally, it can be also coupled with the probabilistic deformation model to further improve shape correspondence. We provide qualitative and quantitative evaluations of our method for shape correspondence, segmentation, fine-grained classification and synthesis. Our experiments demonstrate superior correspondence and segmentation results than previous state-of-the-art approaches.
    BibTeX
    @article {10.1111:cgf.12694,
    journal = {Computer Graphics Forum},
    title = {{Analysis and Synthesis of 3D Shape Families via Deep-learned Generative Models of Surfaces}},
    author = {Huang, Haibin and Kalogerakis, Evangelos and Marlin, Benjamin},
    year = {2015},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    DOI = {10.1111/cgf.12694}
    }
    URI
    http://dx.doi.org/10.1111/cgf.12694
    Collections
    • 34-Issue 5
    • SGP15: Eurographics Symposium on Geometry Processing (CGF 34-5)

    Eurographics Association copyright © 2013 - 2020 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2020 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA