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Group-pair deep feature learning for multi-view 3d model retrieval

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Abstract

This paper employs Convolutional Neural Networks with pooling module to extract view descriptor of 3D model, and proposes the Group-Pair Deep Feature Learning method for multi-view 3D model retrieval. In the method, view descriptor is learned by the supervised autoencoder and multi-label discriminator to further mine the latent feature and category feature of 3D model. To enhance the discriminative capability of model features, we give the Margin Center Loss that minimizes the intra-class distance and maximize the inter-class distance. Experimental results on ModelNet10 and ModelNet40 datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (Nos.61702310, 62076153), the major fundamental research project of Shandong, China (No.ZR2019ZD03), and the Taishan Scholar Project of Shandong, China.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Xiuxiu Chen, Li Liu, Long Zhang, Huaxiang Zhang, Lili Meng & Dongmei Liu

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  1. Xiuxiu Chen
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  2. Li Liu
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  3. Long Zhang
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  4. Huaxiang Zhang
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  5. Lili Meng
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  6. Dongmei Liu
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Correspondence to Li Liu.

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Chen, X., Liu, L., Zhang, L. et al. Group-pair deep feature learning for multi-view 3d model retrieval. Appl Intell 52, 2013–2022 (2022). https://doi.org/10.1007/s10489-021-02471-7

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