Progressive Shape-Distribution-Encoder for Learning 3D Shape Representation

Jin Xie, Fan Zhu, Guoxian Dai, Ling Shao, Yi Fang

    Research output: Contribution to journalArticle

    Abstract

    Since there are complex geometric variations with 3D shapes, extracting efficient 3D shape features is one of the most challenging tasks in shape matching and retrieval. In this paper, we propose a deep shape descriptor by learning shape distributions at different diffusion time via a progressive shape-distribution-encoder (PSDE). First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometry structures of 3D shapes. Then, we propose to learn a deep shape feature through an unsupervised PSDE. Specially, the unsupervised PSDE aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. In order to characterize the intrinsic structures of 3D shapes more efficiently, we stack multiple PSDEs to form a network structure. Finally, we concatenate all neurons in the middle hidden layers of the unsupervised PSDE network to form an unsupervised shape descriptor for retrieval. Furthermore, by imposing an additional constraint on the outputs of all hidden layers, we propose a supervised PSDE to form a supervised shape descriptor. For each hidden layer, the similarity between a pair of outputs from the same class is as large as possible and the similarity between a pair of outputs from different classes is as small as possible. The proposed method is evaluated on three benchmark 3D shape data sets with large geometric variations, i.e., McGill, SHREC'10 ShapeGoogle, and SHREC'14 Human data sets, and the experimental results demonstrate the superiority of the proposed method to the existing approaches.

    Original languageEnglish (US)
    Article number7812756
    Pages (from-to)1231-1242
    Number of pages12
    JournalIEEE Transactions on Image Processing
    Volume26
    Issue number3
    DOIs
    StatePublished - Mar 1 2017

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    Keywords

    • 3D shape retrieval
    • denoising auto-encoder
    • heat diffusion
    • heat kernel signature
    • shape descriptor

    ASJC Scopus subject areas

    • Software
    • Computer Graphics and Computer-Aided Design

    Cite this

    Progressive Shape-Distribution-Encoder for Learning 3D Shape Representation. / Xie, Jin; Zhu, Fan; Dai, Guoxian; Shao, Ling; Fang, Yi.

    In: IEEE Transactions on Image Processing, Vol. 26, No. 3, 7812756, 01.03.2017, p. 1231-1242.

    Research output: Contribution to journalArticle

    Xie, Jin ; Zhu, Fan ; Dai, Guoxian ; Shao, Ling ; Fang, Yi. / Progressive Shape-Distribution-Encoder for Learning 3D Shape Representation. In: IEEE Transactions on Image Processing. 2017 ; Vol. 26, No. 3. pp. 1231-1242.
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