Progressive shape-distribution-encoder for 3D shape retrieval

Jin Xie, Fan Zhu, Guoxian Dai, Yi Fang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we propose a deep shape descriptor by learning the shape distributions at different diffusion time via a progressive deep shape-distribution-encoder. First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometrical structure of the shape. Then, we propose to learn discriminative shape features through a progressive shapedistribution-encoder. Specially, the progressive shape-distributionencoder aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. Furthermore, in order to characterize the intrinsic structure of the shape more efficiently, we stack multiple proposed progressive shapedistribution-encoders to form a neural network structure. Finally, we concatenated all neurons in the hidden layers of the progressive shape-distribution-encoder network to form a discriminative shape descriptor for retrieval. The proposed method is evaluated on three benchmark 3D shape datasets and the experimental results demonstrate the superiority of our method to the existing approaches.

    Original languageEnglish (US)
    Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
    PublisherAssociation for Computing Machinery, Inc
    Pages1167-1170
    Number of pages4
    ISBN (Electronic)9781450334594
    DOIs
    StatePublished - Oct 13 2015
    Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
    Duration: Oct 26 2015Oct 30 2015

    Other

    Other23rd ACM International Conference on Multimedia, MM 2015
    CountryAustralia
    CityBrisbane
    Period10/26/1510/30/15

    Fingerprint

    Electric power distribution
    Neurons
    Neural networks

    Keywords

    • 3D shape retrieval
    • Denoising Auto-Encoder
    • Heat Diffusion
    • Heat Kernel Signature

    ASJC Scopus subject areas

    • Media Technology
    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Software

    Cite this

    Xie, J., Zhu, F., Dai, G., & Fang, Y. (2015). Progressive shape-distribution-encoder for 3D shape retrieval. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference (pp. 1167-1170). Association for Computing Machinery, Inc. https://doi.org/10.1145/2733373.2806308

    Progressive shape-distribution-encoder for 3D shape retrieval. / Xie, Jin; Zhu, Fan; Dai, Guoxian; Fang, Yi.

    MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc, 2015. p. 1167-1170.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Xie, J, Zhu, F, Dai, G & Fang, Y 2015, Progressive shape-distribution-encoder for 3D shape retrieval. in MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc, pp. 1167-1170, 23rd ACM International Conference on Multimedia, MM 2015, Brisbane, Australia, 10/26/15. https://doi.org/10.1145/2733373.2806308
    Xie J, Zhu F, Dai G, Fang Y. Progressive shape-distribution-encoder for 3D shape retrieval. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc. 2015. p. 1167-1170 https://doi.org/10.1145/2733373.2806308
    Xie, Jin ; Zhu, Fan ; Dai, Guoxian ; Fang, Yi. / Progressive shape-distribution-encoder for 3D shape retrieval. MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc, 2015. pp. 1167-1170
    @inproceedings{d9f51ea344ff4dd78d5a1bb1de6ab6b4,
    title = "Progressive shape-distribution-encoder for 3D shape retrieval",
    abstract = "In this paper, we propose a deep shape descriptor by learning the shape distributions at different diffusion time via a progressive deep shape-distribution-encoder. First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometrical structure of the shape. Then, we propose to learn discriminative shape features through a progressive shapedistribution-encoder. Specially, the progressive shape-distributionencoder aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. Furthermore, in order to characterize the intrinsic structure of the shape more efficiently, we stack multiple proposed progressive shapedistribution-encoders to form a neural network structure. Finally, we concatenated all neurons in the hidden layers of the progressive shape-distribution-encoder network to form a discriminative shape descriptor for retrieval. The proposed method is evaluated on three benchmark 3D shape datasets and the experimental results demonstrate the superiority of our method to the existing approaches.",
    keywords = "3D shape retrieval, Denoising Auto-Encoder, Heat Diffusion, Heat Kernel Signature",
    author = "Jin Xie and Fan Zhu and Guoxian Dai and Yi Fang",
    year = "2015",
    month = "10",
    day = "13",
    doi = "10.1145/2733373.2806308",
    language = "English (US)",
    pages = "1167--1170",
    booktitle = "MM 2015 - Proceedings of the 2015 ACM Multimedia Conference",
    publisher = "Association for Computing Machinery, Inc",

    }

    TY - GEN

    T1 - Progressive shape-distribution-encoder for 3D shape retrieval

    AU - Xie, Jin

    AU - Zhu, Fan

    AU - Dai, Guoxian

    AU - Fang, Yi

    PY - 2015/10/13

    Y1 - 2015/10/13

    N2 - In this paper, we propose a deep shape descriptor by learning the shape distributions at different diffusion time via a progressive deep shape-distribution-encoder. First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometrical structure of the shape. Then, we propose to learn discriminative shape features through a progressive shapedistribution-encoder. Specially, the progressive shape-distributionencoder aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. Furthermore, in order to characterize the intrinsic structure of the shape more efficiently, we stack multiple proposed progressive shapedistribution-encoders to form a neural network structure. Finally, we concatenated all neurons in the hidden layers of the progressive shape-distribution-encoder network to form a discriminative shape descriptor for retrieval. The proposed method is evaluated on three benchmark 3D shape datasets and the experimental results demonstrate the superiority of our method to the existing approaches.

    AB - In this paper, we propose a deep shape descriptor by learning the shape distributions at different diffusion time via a progressive deep shape-distribution-encoder. First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometrical structure of the shape. Then, we propose to learn discriminative shape features through a progressive shapedistribution-encoder. Specially, the progressive shape-distributionencoder aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. Furthermore, in order to characterize the intrinsic structure of the shape more efficiently, we stack multiple proposed progressive shapedistribution-encoders to form a neural network structure. Finally, we concatenated all neurons in the hidden layers of the progressive shape-distribution-encoder network to form a discriminative shape descriptor for retrieval. The proposed method is evaluated on three benchmark 3D shape datasets and the experimental results demonstrate the superiority of our method to the existing approaches.

    KW - 3D shape retrieval

    KW - Denoising Auto-Encoder

    KW - Heat Diffusion

    KW - Heat Kernel Signature

    UR - http://www.scopus.com/inward/record.url?scp=84962916582&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84962916582&partnerID=8YFLogxK

    U2 - 10.1145/2733373.2806308

    DO - 10.1145/2733373.2806308

    M3 - Conference contribution

    SP - 1167

    EP - 1170

    BT - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

    PB - Association for Computing Machinery, Inc

    ER -