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View-Based 3D Model Retrieval via Multi-graph Matching

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Abstract

With the rapid development of 3D technology, 3D model retrieval has attracted a large amount of interest in computer vision field. In this paper, we propose a composition-based multi-graph matching method in this paper. Firstly, compute the pairwise matching affinity one-to-one graph matching. Secondly, seek the optimal intermediate graph by diverse graph matching orders, according to the consistency of global matching. Finally, the classic optimization method is used to get the best matching result for similarity measurement. We validate our approach using ETH, NTU and MV-RED 3D model datasets with convolutional neural network features. Extensive experiments show the superiority of the proposed method.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61772359, in part by the Tianjin Research Program of Application Foundation and Advanced Technology under Grant15JCYBJC16200.

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Authors and Affiliations

  1. The School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Weizhi Nie, Anan Liu, Yahui Hao & Yuting Su

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  1. Weizhi Nie
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  2. Anan Liu
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  3. Yahui Hao
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  4. Yuting Su
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Correspondence to Anan Liu or Yahui Hao.

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Nie, W., Liu, A., Hao, Y. et al. View-Based 3D Model Retrieval via Multi-graph Matching. Neural Process Lett 48, 1395–1404 (2018). https://doi.org/10.1007/s11063-017-9717-0

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