{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:47:19Z","timestamp":1771516039937,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XDJK2019C097"],"award-info":[{"award-number":["XDJK2019C097"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Education Reform Project in Southwest University","award":["2019JY046"],"award-info":[{"award-number":["2019JY046"]}]},{"name":"the National Key Research and Development Program of China","award":["2018YFB1004201"],"award-info":[{"award-number":["2018YFB1004201"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11042-022-12281-9","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T20:02:26Z","timestamp":1644955346000},"page":"10407-10426","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-view 3D model retrieval based on enhanced detail features with contrastive center loss"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5285-3190","authenticated-orcid":false,"given":"Qiang","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8780-3994","authenticated-orcid":false,"given":"Yinong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"12281_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/J.PATREC.2015.06.022","volume":"65","author":"S Bai","year":"2015","unstructured":"Bai S, Bai X, Liu W, Roli F (2015) Neural shape codes for 3D model retrieval. Pattern Recognit Lett 65:15\u201321. https:\/\/doi.org\/10.1016\/J.PATREC.2015.06.022","journal-title":"Pattern Recognit Lett"},{"issue":"12","key":"12281_CR2","doi-asserted-by":"publisher","first-page":"2361","DOI":"10.1109\/TPAMI.2015.2424863","volume":"37","author":"X Bai","year":"2015","unstructured":"Bai X, Bai S, Zhu Z, Latecki L (2015) 3D shape matching via two layer coding. IEEE Trans Pattern Anal Mach Intell 37(12):2361\u20132373. https:\/\/doi.org\/10.1109\/TPAMI.2015.2424863","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"12281_CR3","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1109\/TMM.2017.2652071","volume":"19","author":"S Bai","year":"2017","unstructured":"Bai S, Bai X, Zhou Z, Zhang Z, Tian Q, Latecki L (2017) GIFT: towards scalable 3D shape retrieval. IEEE Trans Multimed 19(6):1257\u20131271. https:\/\/doi.org\/10.1109\/TMM.2017.2652071","journal-title":"IEEE Trans Multimed"},{"key":"12281_CR4","unstructured":"Chang A, Funkhouser T, Guibas L, Hanrahan P, Huang Q-X, Li Z et al (2015) ShapeNet: An Information-Rich 3D Model Repository. arXiv preprint arXiv:1512.03012"},{"key":"12281_CR5","doi-asserted-by":"publisher","unstructured":"Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: delving deep into convolutional nets. 2014 British Machine Vision Conference. https:\/\/doi.org\/10.5244\/C.28.6","DOI":"10.5244\/C.28.6"},{"key":"12281_CR6","doi-asserted-by":"publisher","first-page":"102070","DOI":"10.1016\/J.SIMPAT.2020.102070","volume":"102","author":"Y Chen","year":"2020","unstructured":"Chen Y (2020) IoT cloud big data and AI in interdisciplinary domains. Simul Model Pract Theory 102:102070. https:\/\/doi.org\/10.1016\/J.SIMPAT.2020.102070","journal-title":"Simul Model Pract Theory"},{"issue":"1","key":"12281_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.37965\/JAIT.2020.0065","volume":"1","author":"Y Chen","year":"2021","unstructured":"Chen Y, Luca G (2021) Technologies supporting artificial intelligence and robotics application development. J Artif Intell Technol 1(1):1\u20138. https:\/\/doi.org\/10.37965\/JAIT.2020.0065","journal-title":"J Artif Intell Technol"},{"issue":"3","key":"12281_CR8","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1111\/1467-8659.00669","volume":"22","author":"D-Y Chen","year":"2003","unstructured":"Chen D-Y, Tian X-P, Shen Y-T, Ouhyoung M (2003) On Visual Similarity Based 3D Model Retrieval. Computer Graphics Forum 22(3):223\u2013232. https:\/\/doi.org\/10.1111\/1467-8659.00669","journal-title":"Computer Graphics Forum"},{"issue":"13","key":"12281_CR9","doi-asserted-by":"publisher","first-page":"4907","DOI":"10.1007\/S11042-013-1850-9","volume":"74","author":"Q Chen","year":"2015","unstructured":"Chen Q, Fang B, Yu Y-M, Tang Y (2015) 3D CAD model retrieval based on the combination of features. Multimed Tools Appl 74(13):4907\u20134925. https:\/\/doi.org\/10.1007\/S11042-013-1850-9","journal-title":"Multimed Tools Appl"},{"key":"12281_CR10","doi-asserted-by":"publisher","unstructured":"Feng Y, Zhang Z, Zhao X, Ji R, Gao Y (2018) GVCNN: group-view convolutional neural networks for 3D shape recognition. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 264-272. https:\/\/doi.org\/10.1109\/CVPR.2018.00035","DOI":"10.1109\/CVPR.2018.00035"},{"key":"12281_CR11","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/J.INFFUS.2020.11.002","volume":"68","author":"D Fernandes","year":"2021","unstructured":"Fernandes D, Silva A, N\u00e9voa R, Sim\u00f5es C, Gonzalez D, Guevara M et al (2021) Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy. Inf Fusion 68:161\u2013191. https:\/\/doi.org\/10.1016\/J.INFFUS.2020.11.002","journal-title":"Inf Fusion"},{"key":"12281_CR12","doi-asserted-by":"publisher","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. 2006 IEEE Conference on Computer Vision and PatternRecognition, pp 1735-1742. https:\/\/doi.org\/10.1109\/CVPR.2006.100","DOI":"10.1109\/CVPR.2006.100"},{"issue":"2","key":"12281_CR13","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1109\/TIP.2018.2868426","volume":"28","author":"Z Han","year":"2019","unstructured":"Han Z, Shang M, Liu Z, Vong C-M, Liu Y-S, Zwicker M et al (2019) SeqViews2SeqLabels: learning 3D global features via aggregating sequential views by RNN with attention. IEEE Trans Image Process 28(2):658\u2013672. https:\/\/doi.org\/10.1109\/TIP.2018.2868426","journal-title":"IEEE Trans Image Process"},{"key":"12281_CR14","doi-asserted-by":"publisher","unstructured":"He X, Zhou Y, Zhou Z, Bai S, Bai X (2018) Triplet-center loss for multi-view 3D object retrieval. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1945-1954. https:\/\/doi.org\/10.1109\/CVPR.2018.00208","DOI":"10.1109\/CVPR.2018.00208"},{"key":"12281_CR15","doi-asserted-by":"publisher","unstructured":"Jiang J, Bao D, Chen Z, Zhao X, Gao Y (2019) MLVCNN: Multi-loop-view convolutional neural network for 3D shape retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp 8513-8520. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018513","DOI":"10.1609\/aaai.v33i01.33018513"},{"key":"12281_CR16","doi-asserted-by":"publisher","unstructured":"Kanezaki A, Matsushita Y, Nishida Y (2018) RotationNet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5010-5019. https:\/\/doi.org\/10.1109\/CVPR.2018.00526","DOI":"10.1109\/CVPR.2018.00526"},{"issue":"6","key":"12281_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"12281_CR18","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/J.CAG.2020.02.001","volume":"87","author":"S Lengauer","year":"2020","unstructured":"Lengauer S, Komar A, Labrada A, Karl S, Trinkl E, Preiner R et al (2020) A sketch-aided retrieval approach for incomplete 3D objects. Comput Graph 87:111\u2013122. https:\/\/doi.org\/10.1016\/J.CAG.2020.02.001","journal-title":"Comput Graph"},{"issue":"3","key":"12281_CR19","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1007\/S11042-011-0873-3","volume":"62","author":"B Li","year":"2013","unstructured":"Li B, Johan H (2013) 3D model retrieval using hybrid features and class information. Multimed Tools Appl 62(3):821\u2013846. https:\/\/doi.org\/10.1007\/S11042-011-0873-3","journal-title":"Multimed Tools Appl"},{"key":"12281_CR20","unstructured":"Li B, Lu Y, Li C, Godil A, Schreck T, Aono M et al (2014) SHREC\u201914 Track: Large Scale Comprehensive 3D Shape Retrieval. Co-event of the 35rd Annual Conference of the European Association for Computer Graphics (Eurographics 2014)"},{"key":"12281_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/J.CVIU.2014.10.006","volume":"131","author":"B Li","year":"2015","unstructured":"Li B, Lu Y, Li C, Godil A, Schreck T, Aono M et al (2015) A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries. Comput Vis Image Underst 131:1\u201327. https:\/\/doi.org\/10.1016\/J.CVIU.2014.10.006","journal-title":"Comput Vis Image Underst"},{"key":"12281_CR22","doi-asserted-by":"publisher","unstructured":"Li Z, Xu C, Leng B (2019) Angular triplet-center loss for multi-view 3D shape retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp 8682-8689. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018682","DOI":"10.1609\/aaai.v33i01.33018682"},{"issue":"8","key":"12281_CR23","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1007\/S00138-013-0501-5","volume":"24","author":"Z Lian","year":"2013","unstructured":"Lian Z, Godil A, Sun X, Xiao J (2013) CM-BOF: visual similarity-based 3D shape retrieval using Clock Matching and Bag-of-Features. Mach Vis Appl 24(8):1685\u20131704. https:\/\/doi.org\/10.1007\/S00138-013-0501-5","journal-title":"Mach Vis Appl"},{"issue":"11","key":"12281_CR24","doi-asserted-by":"publisher","first-page":"2447","DOI":"10.1016\/J.PATCOG.2009.04.024","volume":"42","author":"A Mademlis","year":"2009","unstructured":"Mademlis A, Daras P, Tzovaras D, Strintzis M (2009) 3D object retrieval using the 3D shape impact descriptor. Pattern Recogn 42(11):2447\u20132459. https:\/\/doi.org\/10.1016\/J.PATCOG.2009.04.024","journal-title":"Pattern Recogn"},{"issue":"7","key":"12281_CR25","doi-asserted-by":"publisher","first-page":"3593","DOI":"10.1007\/S11042-014-2191-Z","volume":"75","author":"K Makantasis","year":"2016","unstructured":"Makantasis K, Doulamis A, Doulamis N, Ioannides M (2016) In the wild image retrieval and clustering for 3D cultural heritage landmarks reconstruction. Multimed Tools Appl 75(7):3593\u20133629. https:\/\/doi.org\/10.1007\/S11042-014-2191-Z","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"12281_CR26","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/S11263-009-0281-6","volume":"89","author":"P Papadakis","year":"2010","unstructured":"Papadakis P, Pratikakis I, Theoharis T, Perantonis S (2010) PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval. Int J Comput Vision 89(2):177\u2013192. https:\/\/doi.org\/10.1007\/S11263-009-0281-6","journal-title":"Int J Comput Vision"},{"key":"12281_CR27","doi-asserted-by":"publisher","unstructured":"Qi C, Su F (2017) Contrastive-center loss for deep neural networks. 2017 IEEE International Conference on Image Processing, pp 2851-2855. https:\/\/doi.org\/10.1109\/ICIP.2017.8296803","DOI":"10.1109\/ICIP.2017.8296803"},{"key":"12281_CR28","doi-asserted-by":"publisher","first-page":"102053","DOI":"10.1016\/J.DISPLA.2021.102053","volume":"69","author":"S Qi","year":"2021","unstructured":"Qi S, Ning X, Yang G, Zhang L, Long P, Cai W, Li W (2021) Review of multi-view 3D object recognition methods based on deep learning. Displays 69:102053. https:\/\/doi.org\/10.1016\/J.DISPLA.2021.102053","journal-title":"Displays"},{"key":"12281_CR29","doi-asserted-by":"publisher","unstructured":"Savva M, Yu F, Su H, Aono M, Chen B, Cohen-Or D et al (2016) Large-scale 3D shape retrieval from ShapeNet core55. 3DOR \u201816 Proceedings of the Eurographics 2016 Workshop on 3D Object Retrieval, pp 89-98. https:\/\/doi.org\/10.2312\/3DOR.20161092","DOI":"10.2312\/3DOR.20161092"},{"key":"12281_CR30","doi-asserted-by":"publisher","unstructured":"Savva M, Yu F, Su H, Kanezaki A, Furuya T, Ohbuchi R et al (2017) Large-scale 3D shape retrieval from ShapeNet Core55: SHREC\u201917 track. 3Dor \u201817 Proceedings of the Workshop on 3D Object Retrieval, pp 39-50. https:\/\/doi.org\/10.2312\/3DOR.20171050","DOI":"10.2312\/3DOR.20171050"},{"key":"12281_CR31","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/J.CAG.2017.12.001","volume":"71","author":"K Sfikas","year":"2018","unstructured":"Sfikas K, Pratikakis I, Theoharis T (2018) Ensemble of PANORAMA-based convolutional neural networks for 3D model classification and retrieval. Comput Graph 71:208\u2013218. https:\/\/doi.org\/10.1016\/J.CAG.2017.12.001","journal-title":"Comput Graph"},{"issue":"12","key":"12281_CR32","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1109\/LSP.2015.2480802","volume":"22","author":"B Shi","year":"2015","unstructured":"Shi B, Bai S, Zhou Z, Bai X (2015) DeepPano: Deep panoramic representation for 3-D shape recognition. IEEE Signal Process Lett 22(12):2339\u20132343. https:\/\/doi.org\/10.1109\/LSP.2015.2480802","journal-title":"IEEE Signal Process Lett"},{"key":"12281_CR33","doi-asserted-by":"publisher","unstructured":"Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The Princeton Shape Benchmark. Proceedings Shape Modeling Applications 2004, pp167-178. https:\/\/doi.org\/10.1109\/SMI.2004.1314504","DOI":"10.1109\/SMI.2004.1314504"},{"key":"12281_CR34","doi-asserted-by":"publisher","unstructured":"Su H, Maji S, Kalogerakis E, Learned-Miller E (2015) Multi-view convolutional neural networks for 3D shape recognition. 2015 IEEE International Conference on Computer Vision, pp 945-953. https:\/\/doi.org\/10.1109\/ICCV.2015.114","DOI":"10.1109\/ICCV.2015.114"},{"issue":"3","key":"12281_CR35","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/S11042-007-0181-0","volume":"39","author":"J Tangelder","year":"2008","unstructured":"Tangelder J, Veltkamp R (2008) A survey of content based 3D shape retrieval methods. Multimed Tools Appl 39(3):441\u2013471. https:\/\/doi.org\/10.1007\/S11042-007-0181-0","journal-title":"Multimed Tools Appl"},{"key":"12281_CR36","doi-asserted-by":"publisher","unstructured":"Vranic D (2005) DESIRE: a composite 3D-shape descriptor. 2005 IEEE International Conference on Multimedia and Expo, pp 962-965. https:\/\/doi.org\/10.1109\/ICME.2005.1521584","DOI":"10.1109\/ICME.2005.1521584"},{"issue":"5","key":"12281_CR37","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sun Y, Liu Z, Sarma S, Bronstein M, Solomon J (2019) Dynamic graph CNN for learning on point clouds. ACM Trans Graph 38(5):146. https:\/\/doi.org\/10.1145\/3326362","journal-title":"ACM Trans Graph"},{"key":"12281_CR38","doi-asserted-by":"publisher","unstructured":"Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. 2016 European Conference on Computer Vision, pp 499-515. https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"12281_CR39","doi-asserted-by":"publisher","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3D ShapeNets: A deep representation forvolumetric shapes. 2015 IEEE Conference on Computer Vision and PatternRecognition, pp 1912-1920. https:\/\/doi.org\/10.1109\/CVPR.2015.7298801","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"12281_CR40","doi-asserted-by":"publisher","unstructured":"You H, Feng Y, Zhao X, Zou C, Ji R, Gao Y (2019) PVRNet: point-view relation neural network for 3D shape recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp 9119-9126. https:\/\/doi.org\/10.1609\/aaai.v33i01.33019119","DOI":"10.1609\/aaai.v33i01.33019119"},{"issue":"1","key":"12281_CR41","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1155\/2007\/23912","volume":"2007","author":"D Zarpalas","year":"2007","unstructured":"Zarpalas D, Daras P, Axenopoulos A, Tzovaras D, Strintzis M (2007) 3D model search and retrieval using the spherical trace transform. EURASIP J Adv Signal Process 2007(1):207\u2013207. https:\/\/doi.org\/10.1155\/2007\/23912","journal-title":"EURASIP J Adv Signal Process"},{"key":"12281_CR42","doi-asserted-by":"publisher","unstructured":"Zhen L, Hu P, Wang X, Peng D (2019) Deep supervised cross-modal retrieval. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10394-10403. https:\/\/doi.org\/10.1109\/CVPR.2019.01064","DOI":"10.1109\/CVPR.2019.01064"},{"issue":"204","key":"12281_CR43","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/J.NEUCOM.2015.08.127","volume":"204","author":"Z Zhu","year":"2016","unstructured":"Zhu Z, Wang X, Bai S, Yao C, Bai X (2016) Deep learning representation using autoencoder for 3D shape retrieval. Neurocomputing 204(204):41\u201350. https:\/\/doi.org\/10.1016\/J.NEUCOM.2015.08.127","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12281-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12281-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12281-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T11:11:40Z","timestamp":1648552300000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12281-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,15]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["12281"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12281-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,15]]},"assertion":[{"value":"13 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2022","order":4,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/Competing interests"}}]}}