Learning cross-domain neural networks for sketch-based 3D shape retrieval

Fan Zhu, Jin Xie, Yi Fang

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

    Abstract

    Sketch-based 3D shape retrieval, which returns a set of relevant 3D shapes based on users' input sketch queries, has been receiving increasing attentions in both graphics community and vision community. In this work, we address the sketch-based 3D shape retrieval problem with a novel Cross- Domain Neural Networks (CDNN) approach, which is further extended to Pyramid Cross-Domain Neural Networks (PCDNN) by cooperating with a hierarchical structure. In order to alleviate the discrepancies between sketch features and 3D shape features, a neural network pair that forces identical representations at the target layer for instances of the same class is trained for sketches and 3D shapes respectively. By constructing cross-domain neural networks at multiple pyramid levels, a many-To-one relationship is established between a 3D shape feature and sketch features extracted from different scales. We evaluate the effectiveness of both CDNN and PCDNN approach on the extended large-scale SHREC 2014 benchmark and compare with some other well established methods. Experimental results suggest that both CDNN and PCDNN can outperform state-of-The-Art performance, where PCDNN can further improve CDNN when employing a hierarchical structure.

    Original languageEnglish (US)
    Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
    PublisherAAAI press
    Pages3683-3689
    Number of pages7
    ISBN (Electronic)9781577357605
    StatePublished - Jan 1 2016
    Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
    Duration: Feb 12 2016Feb 17 2016

    Other

    Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
    CountryUnited States
    CityPhoenix
    Period2/12/162/17/16

    Fingerprint

    Neural networks

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Zhu, F., Xie, J., & Fang, Y. (2016). Learning cross-domain neural networks for sketch-based 3D shape retrieval. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 3683-3689). AAAI press.

    Learning cross-domain neural networks for sketch-based 3D shape retrieval. / Zhu, Fan; Xie, Jin; Fang, Yi.

    30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. p. 3683-3689.

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

    Zhu, F, Xie, J & Fang, Y 2016, Learning cross-domain neural networks for sketch-based 3D shape retrieval. in 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, pp. 3683-3689, 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, United States, 2/12/16.
    Zhu F, Xie J, Fang Y. Learning cross-domain neural networks for sketch-based 3D shape retrieval. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press. 2016. p. 3683-3689
    Zhu, Fan ; Xie, Jin ; Fang, Yi. / Learning cross-domain neural networks for sketch-based 3D shape retrieval. 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. pp. 3683-3689
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