{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:10:27Z","timestamp":1773828627668,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673142"],"award-info":[{"award-number":["61673142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hei LongJiang Province of China","award":["LH2022F029"],"award-info":[{"award-number":["LH2022F029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00371-023-03098-0","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T05:01:31Z","timestamp":1695877291000},"page":"4539-4551","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["3D shape classification based on global and local features extraction with collaborative learning"],"prefix":"10.1007","volume":"40","author":[{"given":"Bo","family":"Ding","sequence":"first","affiliation":[]},{"given":"Libao","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5156-651X","authenticated-orcid":false,"given":"Yongjun","family":"He","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"3098_CR1","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1109\/TIP.2020.3048623","volume":"30","author":"X Liu","year":"2021","unstructured":"Liu, X., Han, Z., Liu, Y.S., et al.: Fine-grained 3D shape classification with hierarchical part-view attention. IEEE Trans. Image Process. 30, 1744\u20131758 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3098_CR2","doi-asserted-by":"crossref","unstructured":"Wei, X., Yu, R., Sun, J.: View-gcn: view-based graph convolutional network for 3d shape analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1850\u20131859 (2020)","DOI":"10.1109\/CVPR42600.2020.00192"},{"key":"3098_CR3","doi-asserted-by":"crossref","unstructured":"Gao, Z., Shao, Y., Guan, W. et al.: A novel patch convolutional neural network for view-based 3D Model Retrieval. Proceedings of the 29th ACM International Conference on Multimedia. 2021: 2699\u20132707.","DOI":"10.1145\/3474085.3475450"},{"key":"3098_CR4","unstructured":"Hegde, V., Zadeh, R.: Fusionnet: 3d object classification using multiple data representations. arXiv preprint arXiv:1607.05695 (2016)"},{"key":"3098_CR5","unstructured":"Wu, Z., Song, S., Khosla, A. et al.: 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1912\u20131920 (2015)"},{"key":"3098_CR6","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: Voxnet: a 3d convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp. 922\u2013928 (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"3098_CR7","doi-asserted-by":"crossref","unstructured":"Liu, S., Giles, L., Ororbia, A.: Learning a hierarchical latent-variable model of 3d shapes. In: 2018 International Conference on 3D Vision (3DV). IEEE, pp. 542\u2013551 (2018)","DOI":"10.1109\/3DV.2018.00068"},{"key":"3098_CR8","unstructured":"Qi, C. R., Su, H., Mo, K. et al.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652\u2013660 (2017)"},{"key":"3098_CR9","unstructured":"Qi, C. R., Yi, L., Su, H. et al.: PointNet++ deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st international conference on neural information processing systems. pp. 5105\u20135114 (2017)"},{"key":"3098_CR10","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B. M., Lee, G. H.: So-net: self-organizing network for point cloud analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9397\u20139406 (2018)","DOI":"10.1109\/CVPR.2018.00979"},{"key":"3098_CR11","doi-asserted-by":"crossref","unstructured":"Klokov, R., Lempitsky, V.: Escape from cells: deep kd-networks for the recognition of 3d point cloud models. In: Proceedings of the IEEE international conference on computer vision. pp. 863\u2013872 (2017)","DOI":"10.1109\/ICCV.2017.99"},{"issue":"5","key":"3098_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., et al.: Dynamic graph cnn for learning on point clouds. Acm Trans. Graph (tog) 38(5), 1\u201312 (2019)","journal-title":"Acm Trans. Graph (tog)"},{"key":"3098_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S. et al.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 8895\u20138904 (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"key":"3098_CR14","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E. et al.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision. pp. 945\u2013953 (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"3098_CR15","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhang, Z., Zhao, X. et al.: Gvcnn: Group-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 264\u2013272 (2018)","DOI":"10.1109\/CVPR.2018.00035"},{"key":"3098_CR16","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TIP.2020.3039378","volume":"30","author":"K Sun","year":"2020","unstructured":"Sun, K., Zhang, J., Liu, J., et al.: DRCNN: dynamic routing convolutional neural network for multi-view 3D object recognition. IEEE Trans. Image Process. 30, 868\u2013877 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"3098_CR17","first-page":"303","volume":"31","author":"B Jing","year":"2019","unstructured":"Jing, B., Qing, L., Wei, F.: 3D model classification and retrieval based on CNN voting scheme. J. Comput.-Aided Des. Comput. Graph. 31(2), 303\u2013314 (2019)","journal-title":"J. Comput.-Aided Des. Comput. Graph."},{"key":"3098_CR18","doi-asserted-by":"crossref","unstructured":"Ding, B., Tang, L., He, Y. J.: An efficient 3D model retrieval method based on convolutional neural network. Complexity, pp. 1\u201314 (2020)","DOI":"10.1155\/2020\/9050459"},{"key":"3098_CR19","doi-asserted-by":"publisher","first-page":"200812","DOI":"10.1109\/ACCESS.2020.3035583","volume":"8","author":"B Ding","year":"2020","unstructured":"Ding, B., Tang, L., Gao, Z., et al.: 3D shape classification using a single view. IEEE Access 8, 200812\u2013200822 (2020)","journal-title":"IEEE Access"},{"key":"3098_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108774","volume":"129","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Zhang, Y., Gao, J., et al.: VFMVAC: view-filtering-based multi-view aggregating convolution for 3D shape recognition and retrieval. Pattern Recogn. 129, 108774 (2022)","journal-title":"Pattern Recogn."},{"key":"3098_CR21","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.patrec.2021.07.010","volume":"150","author":"Q Liang","year":"2021","unstructured":"Liang, Q., Li, Q., Zhang, L., et al.: MHFP: multi-view based hierarchical fusion pooling method for 3D shape recognition. Pattern Recogn. Lett. 150, 214\u2013220 (2021)","journal-title":"Pattern Recogn. Lett."},{"key":"3098_CR22","doi-asserted-by":"publisher","first-page":"155939","DOI":"10.1109\/ACCESS.2020.3018875","volume":"8","author":"AA Liu","year":"2020","unstructured":"Liu, A.A., Guo, F.B., Zhou, H.Y., et al.: Semantic and context information fusion network for view-based 3D model classification and retrieval. IEEE Access 8, 155939\u2013155950 (2020)","journal-title":"IEEE Access"},{"key":"3098_CR23","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.neunet.2020.02.017","volume":"125","author":"Z Gao","year":"2020","unstructured":"Gao, Z., Xue, H., Wan, S.: Multiple discrimination and pairwise CNN for view-based 3D object retrieval. Neural Netw. 125, 290\u2013302 (2020)","journal-title":"Neural Netw."},{"key":"3098_CR24","doi-asserted-by":"crossref","unstructured":"Kanezaki, A., Matsushita, Y., Nishida, Y.: Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5010\u20135019 (2018)","DOI":"10.1109\/CVPR.2018.00526"},{"issue":"8","key":"3098_CR25","doi-asserted-by":"publisher","first-page":"3986","DOI":"10.1109\/TIP.2019.2904460","volume":"28","author":"Z Han","year":"2019","unstructured":"Han, Z., Lu, H., Liu, Z., et al.: 3D2SeqViews: aggregating sequential views for 3D global feature learning by CNN with hierarchical attention aggregation. IEEE Trans. Image Process. 28(8), 3986\u20133999 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"3098_CR26","doi-asserted-by":"crossref","unstructured":"He, X., Huang, T., Bai, S. et al.: View n-gram network for 3d object retrieval. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 7515\u20137524 (2019)","DOI":"10.1109\/ICCV.2019.00761"},{"issue":"2","key":"3098_CR27","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1109\/TIP.2018.2868426","volume":"28","author":"Z Han","year":"2018","unstructured":"Han, Z., Shang, M., Liu, Z., et al.: SeqViews2SeqLabels: learning 3D global features via aggregating sequential views by RNN with attention. IEEE Trans. Image Process. 28(2), 658\u2013672 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"3098_CR28","doi-asserted-by":"publisher","first-page":"4699","DOI":"10.1007\/s11042-019-7521-8","volume":"79","author":"AA Liu","year":"2020","unstructured":"Liu, A.A., Zhou, H.Y., Li, M.J., et al.: 3D model retrieval based on multi-view attentional convolutional neural network. Multimed. Tools Appl. 79(7), 4699\u20134711 (2020)","journal-title":"Multimed. Tools Appl."},{"key":"3098_CR29","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1016\/j.ins.2020.09.057","volume":"547","author":"AA Liu","year":"2021","unstructured":"Liu, A.A., Zhou, H., Nie, W., et al.: Hierarchical multi-view context modelling for 3D object classification and retrieval. Inf. Sci. 547, 984\u2013995 (2021)","journal-title":"Inf. Sci."},{"key":"3098_CR30","doi-asserted-by":"publisher","first-page":"139792","DOI":"10.1109\/ACCESS.2020.3012692","volume":"8","author":"Q Liang","year":"2020","unstructured":"Liang, Q., Wang, Y., Nie, W., et al.: MVCLN: multi-view convolutional LSTM network for cross-media 3D shape recognition. IEEE Access 8, 139792\u2013139802 (2020)","journal-title":"IEEE Access"},{"key":"3098_CR31","doi-asserted-by":"crossref","unstructured":"Han, Z., Liu, X., Liu, Y. S. et al.: Parts4Feature: learning 3D global features from generally semantic parts in multiple views. In: Twenty-eighth international joint conference on artificial intelligence (IJCAI 2019) (2019)","DOI":"10.24963\/ijcai.2019\/108"},{"key":"3098_CR32","doi-asserted-by":"publisher","first-page":"4530","DOI":"10.1109\/TIP.2020.2967579","volume":"29","author":"R Yu","year":"2020","unstructured":"Yu, R., Sun, J., Li, H.: Second-order spectral transform block for 3D shape classification and retrieval. IEEE Trans. Image Process. 29, 4530\u20134543 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3098_CR33","doi-asserted-by":"crossref","unstructured":"Yu, T., Meng, J., Yuan, J.: Multi-view harmonized bilinear network for 3d object recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 186\u2013194 (2018)","DOI":"10.1109\/CVPR.2018.00027"},{"key":"3098_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P. H. and Koltun, V.: Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16259\u201316268 (2021)","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"3098_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3140355","author":"Y Gao","year":"2022","unstructured":"Gao, Y., Liu, X., Li, J., Fang, Z., Jiang, X., Huq, K.M.S.: Lft-net: local feature transformer network for point clouds analysis. IEEE Trans. Intell. Transp. Syst. (2022). https:\/\/doi.org\/10.1109\/TITS.2022.3140355","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3098_CR36","doi-asserted-by":"crossref","unstructured":"Pan, X., Xia, Z., Song, S., Li, L. E. and Huang, G.:. 3d object detection with pointformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition,pp. 7463\u20137472 (2021)","DOI":"10.1109\/CVPR46437.2021.00738"},{"key":"3098_CR37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y. et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"1","key":"3098_CR38","first-page":"852","volume":"36","author":"J He","year":"2022","unstructured":"He, J., Chen, J.N., Liu, S., et al.: Transfg: a transformer architecture for fine-grained recognition. Proc AAAI Conf Artif Intell 36(1), 852\u2013860 (2022)","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3098_CR39","unstructured":"Hassani, A., Walton, S., Shah, N. et al.: Escaping the big data paradigm with compact transformers. arXiv preprint arXiv:2104.05704, (2021)"},{"key":"3098_CR40","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A. et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: International conference on learning representations (2020)"},{"key":"3098_CR41","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1195\u20131204 (2017)"},{"key":"3098_CR42","unstructured":"Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: pseudo label refinery for unsupervised domain adaptation on person re-identification. In: International Conference on Learning Representations (2019)"},{"key":"3098_CR43","first-page":"1","volume":"01","author":"S Chen","year":"2022","unstructured":"Chen, S., Hong, Z., Hou, W., et al.: TransZero++: cross attribute-guided transformer for zero-shot learning. IEEE Trans. Pattern Anal. Mach. Intell. 01, 1\u201317 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03098-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03098-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03098-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T11:15:45Z","timestamp":1717672545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03098-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["3098"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03098-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]},"assertion":[{"value":"9 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}