Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks
Non-rigid 3-D model retrieval is a challenging problem in 3-D shape analysis. Recently, deep learning-based 3-D feature extraction methods have been studied and have achieved better performance than the previous state-of-the-art methods. Inspired by the quadruplet neural networks proposed for learni...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8543487/ |
| _version_ | 1828885098717511680 |
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| author | Hui Zeng Yanrong Liu Jiwei Liu Dongmei Fu |
| author_facet | Hui Zeng Yanrong Liu Jiwei Liu Dongmei Fu |
| author_sort | Hui Zeng |
| collection | DOAJ |
| description | Non-rigid 3-D model retrieval is a challenging problem in 3-D shape analysis. Recently, deep learning-based 3-D feature extraction methods have been studied and have achieved better performance than the previous state-of-the-art methods. Inspired by the quadruplet neural networks proposed for learning local image feature descriptors, we propose a novel non-rigid 3-D model retrieval method based on quadruplet convolutional neural networks. For training the proposed networks, the quadruplet samples are first selected using the online sampling method. For each 3-D model, the wave kernel signature descriptor of each vertex is computed, and its corresponding multi-energy shape distribution matrix is constructed as the input of the network. Then, the quadruplet convolutional neural networks are trained using our improved quadruplet loss function, which not only preserves the advantages of existing quadruplet loss functions but also decreases the risk of underfitting. For the query sample, the 3-D shape features are computed using one branch of the trained quadruplet networks. Finally, the retrieval results are obtained by the L2 distance measure. Extensive experimental results have validated the effectiveness of the proposed method. |
| first_indexed | 2024-12-13T11:20:19Z |
| format | Article |
| id | doaj.art-f22b5d86cf0a4a79b9391394abcac930 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:20:19Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f22b5d86cf0a4a79b9391394abcac9302022-12-21T23:48:31ZengIEEEIEEE Access2169-35362018-01-016760877609710.1109/ACCESS.2018.28827118543487Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural NetworksHui Zeng0https://orcid.org/0000-0002-4137-7424Yanrong Liu1Jiwei Liu2Dongmei Fu3Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaNon-rigid 3-D model retrieval is a challenging problem in 3-D shape analysis. Recently, deep learning-based 3-D feature extraction methods have been studied and have achieved better performance than the previous state-of-the-art methods. Inspired by the quadruplet neural networks proposed for learning local image feature descriptors, we propose a novel non-rigid 3-D model retrieval method based on quadruplet convolutional neural networks. For training the proposed networks, the quadruplet samples are first selected using the online sampling method. For each 3-D model, the wave kernel signature descriptor of each vertex is computed, and its corresponding multi-energy shape distribution matrix is constructed as the input of the network. Then, the quadruplet convolutional neural networks are trained using our improved quadruplet loss function, which not only preserves the advantages of existing quadruplet loss functions but also decreases the risk of underfitting. For the query sample, the 3-D shape features are computed using one branch of the trained quadruplet networks. Finally, the retrieval results are obtained by the L2 distance measure. Extensive experimental results have validated the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/8543487/Non-rigid 3D model retrievalconvolutional neural networkquadruplet loss functionwave kernel signaturemulti-energy shape distribution |
| spellingShingle | Hui Zeng Yanrong Liu Jiwei Liu Dongmei Fu Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks IEEE Access Non-rigid 3D model retrieval convolutional neural network quadruplet loss function wave kernel signature multi-energy shape distribution |
| title | Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks |
| title_full | Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks |
| title_fullStr | Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks |
| title_full_unstemmed | Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks |
| title_short | Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks |
| title_sort | non rigid 3d model retrieval based on quadruplet convolutional neural networks |
| topic | Non-rigid 3D model retrieval convolutional neural network quadruplet loss function wave kernel signature multi-energy shape distribution |
| url | https://ieeexplore.ieee.org/document/8543487/ |
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