{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T04:43:33Z","timestamp":1768020213937,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61772108"],"award-info":[{"award-number":["61772108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61572096"],"award-info":[{"award-number":["61572096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61733002"],"award-info":[{"award-number":["61733002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"dalian science and technology innovation fund","doi-asserted-by":"crossref","award":["2019J11CY004"],"award-info":[{"award-number":["2019J11CY004"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00530-021-00871-w","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:03:07Z","timestamp":1641686587000},"page":"761-778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Sequential learning for sketch-based 3D model retrieval"],"prefix":"10.1007","volume":"28","author":[{"given":"Hairui","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Caifei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhihui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haojie","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"key":"871_CR1","doi-asserted-by":"publisher","unstructured":"Bai, S., Bai, X., Zhou, Z., Zhang, Z., Latecki, L.J.: GIFT: a real-time and scalable 3D shape search engine. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27\u201330, 2016, pp. 5023\u20135032. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.543","DOI":"10.1109\/CVPR.2016.543"},{"issue":"6","key":"871_CR2","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.J.: GIFT: towards scalable 3D shape retrieval. IEEE Trans. Multimed. 19(6), 1257\u20131271 (2017)","journal-title":"IEEE Trans. Multimed."},{"key":"871_CR3","doi-asserted-by":"publisher","unstructured":"Banchs, R.E.: A comparative evaluation of 2D and 3D visual exploration of document search results. In: A.\u00a0Jaafar, N.M. Ali, S.A.M. Noah, A.F. Smeaton, P.\u00a0Bruza, Z.A. Bakar, N.\u00a0Jamil, T.M.T. Sembok (eds.) Information Retrieval Technology\u201410th Asia Information Retrieval Societies Conference, AIRS 2014, Kuching, Malaysia, December 3\u20135, 2014. Proceedings, Lecture Notes in Computer Science, vol. 8870, pp. 100\u2013111. Springer (2014). https:\/\/doi.org\/10.1007\/978-3-319-12844-3_9","DOI":"10.1007\/978-3-319-12844-3_9"},{"key":"871_CR4","doi-asserted-by":"publisher","unstructured":"Chen, J., Fang, Y.: Deep cross-modality adaptation via semantics preserving adversarial learning for sketch-based 3D shape retrieval. In: V.\u00a0Ferrari, M.\u00a0Hebert, C.\u00a0Sminchisescu, Y.\u00a0Weiss (eds.) Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part XIII, Lecture Notes in Computer Science, vol. 11217, pp. 624\u2013640. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_37","DOI":"10.1007\/978-3-030-01261-8_37"},{"issue":"7","key":"871_CR5","doi-asserted-by":"publisher","first-page":"3374","DOI":"10.1109\/TIP.2018.2817042","volume":"27","author":"G Dai","year":"2018","unstructured":"Dai, G., Xie, J., Fang, Y.: Deep correlated holistic metric learning for sketch-based 3D shape retrieval. IEEE Trans. Image Process. 27(7), 3374\u20133386 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2817042","journal-title":"IEEE Trans. Image Process."},{"key":"871_CR6","unstructured":"Dai, G., Xie, J., Zhu, F., Fang, Y.: Deep correlated metric learning for sketch-based 3D shape retrieval. In: S.P. Singh, S.\u00a0Markovitch (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4\u20139, 2017, San Francisco, California, USA, pp. 4002\u20134008. AAAI Press (2017). http:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14431"},{"issue":"5","key":"871_CR7","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1109\/TIP.2012.2183142","volume":"21","author":"T Darom","year":"2012","unstructured":"Darom, T., Keller, Y.: Scale-invariant features for 3-D mesh models. IEEE Trans. Image Process. 21(5), 2758\u20132769 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"871_CR8","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20\u201325 June 2009, Miami, Florida, USA, pp. 248\u2013255. IEEE Computer Society (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"4","key":"871_CR9","first-page":"44:1","volume":"31","author":"M Eitz","year":"2012","unstructured":"Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31(4), 44:1-44:10 (2012)","journal-title":"ACM Trans. Graph."},{"key":"871_CR10","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhang, Z., Zhao, X., Ji, R., Gao, Y.: GVCNN: group-view convolutional neural networks for 3D shape recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18\u201322, 2018, pp. 264\u2013272. IEEE Computer Society (2018). http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Feng_GVCNN_Group-View_Convolutional_CVPR_2018_paper.html","DOI":"10.1109\/CVPR.2018.00035"},{"key":"871_CR11","doi-asserted-by":"publisher","unstructured":"Furukawa, M., Akagi, Y., Kawai, Y., Kawasaki, H.: Interactive 3D animation creation and viewing system based on motion graph and pose estimation method. In: K.A. Hua, Y.\u00a0Rui, R.\u00a0Steinmetz, A.\u00a0Hanjalic, A.\u00a0Natsev, W.\u00a0Zhu (eds.) Proceedings of the ACM International Conference on Multimedia, MM \u201914, Orlando, FL, USA, November 03\u201307, 2014, pp. 1213\u20131216. ACM (2014). https:\/\/doi.org\/10.1145\/2647868.2655055","DOI":"10.1145\/2647868.2655055"},{"key":"871_CR12","doi-asserted-by":"publisher","unstructured":"Furuya, T., Ohbuchi, R.: Ranking on cross-domain manifold for sketch-based 3D model retrieval. In: X.\u00a0Mao, L.\u00a0Hong (eds.) 2013 International Conference on Cyberworlds, Yokohama, Japan, October 21\u201323, 2013, pp. 274\u2013281. IEEE Computer Society (2013). https:\/\/doi.org\/10.1109\/CW.2013.60","DOI":"10.1109\/CW.2013.60"},{"key":"871_CR13","doi-asserted-by":"crossref","unstructured":"Furuya, T., Ohbuchi, R.: Deep aggregation of local 3D geometric features for 3D model retrieval. In: R.C. Wilson, E.R. Hancock, W.A.P. Smith (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19\u201322, 2016. BMVA Press (2016). http:\/\/www.bmva.org\/bmvc\/2016\/papers\/paper121\/index.html","DOI":"10.5244\/C.30.121"},{"key":"871_CR14","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27\u201330, 2016, pp. 770\u2013778. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"871_CR15","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian, M.H., Jia, W., He, X., Kennedy, P.J.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582\u2013596 (2019)","journal-title":"J. Digit. Imaging"},{"key":"871_CR16","doi-asserted-by":"publisher","unstructured":"Kawamura, S., Usui, K., Furuya, T., Ohbuchi, R.: Local goemetrical feature with spatial context for shape-based 3D model retrieval. In: M.\u00a0Spagnuolo, M.M. Bronstein, A.M. Bronstein, A.\u00a0Ferreira (eds.) 5th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2012, Cagliari, Sardinia, Italy, May 13, 2012, pp. 55\u201358. Eurographics Association (2012). https:\/\/doi.org\/10.2312\/3DOR\/3DOR12\/055-058","DOI":"10.2312\/3DOR\/3DOR12\/055-058"},{"key":"871_CR17","doi-asserted-by":"publisher","unstructured":"Klokov, R., Lempitsky, V.S.: Escape from cells: deep KD-networks for the recognition of 3D point cloud models. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22\u201329, 2017, pp. 863\u2013872. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.99","DOI":"10.1109\/ICCV.2017.99"},{"key":"871_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: P.L. Bartlett, F.C.N. Pereira, C.J.C. Burges, L.\u00a0Bottou, K.Q. Weinberger (eds.) Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3\u20136, 2012, Lake Tahoe, Nevada, United States, pp. 1106\u20131114 (2012). https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"issue":"12","key":"871_CR19","doi-asserted-by":"publisher","first-page":"3164","DOI":"10.1109\/TMM.2019.2918729","volume":"21","author":"Z Kuang","year":"2019","unstructured":"Kuang, Z., Yu, J., Zhu, S., Li, Z., Fan, J.: Effective 3-D shape retrieval by integrating traditional descriptors and pointwise convolution. IEEE Trans. Multimed. 21(12), 3164\u20133177 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"871_CR20","doi-asserted-by":"crossref","unstructured":"Lei, Y., Zhou, Z., Zhang, P., Guo, Y., Ma, Z., Liu, L.: Deep point-to-subspace metric learning for sketch-based 3D shape retrieval. Pattern Recognit. 96, 106981 (2019)","DOI":"10.1016\/j.patcog.2019.106981"},{"key":"871_CR21","unstructured":"Li, B., Lu, Y., Duan, F., Dong, S., Fan, Y., Qian, L., Laga, H., Li, H., Li, Y., Lui, P., Ovsjanikov, M., Tabia, H., Ye, Y., Yin, H., Xu, Z.: Shrec\u201916 track: 3D sketch-based 3D shape retrieval. In: Eurographics Workshop on 3D Object Retrieval (3DOR) (2016)"},{"key":"871_CR22","doi-asserted-by":"publisher","unstructured":"Li, B., Lu, Y., Godil, A., Schreck, T., Aono, M., Johan, H., Saavedra, J.M., Tashiro, S.: Shrec\u201913 track: large scale sketch-based 3D shape retrieval. In: U.\u00a0Castellani, T.\u00a0Schreck, S.\u00a0Biasotti, I.\u00a0Pratikakis, A.\u00a0Godil, R.C. Veltkamp (eds.) 6th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2013, Girona, Spain, May 11, 2013, pp. 89\u201396. Eurographics Association (2013). https:\/\/doi.org\/10.2312\/3DOR\/3DOR13\/089-096","DOI":"10.2312\/3DOR\/3DOR13\/089-096"},{"key":"871_CR23","doi-asserted-by":"publisher","unstructured":"Li, B., Lu, Y., Li, C., Godil, A., Schreck, T., Aono, M., Burtscher, M., Fu, H., Furuya, T., Johan, H., Liu, J., Ohbuchi, R., Tatsuma, A., Zou, C.: Extended large scale sketch-based 3D shape retrieval. In: B.\u00a0Bustos, H.\u00a0Tabia, J.\u00a0Vandeborre, R.C. Veltkamp (eds.) 7th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2014, Strasbourg, France, April 6, 2014, pp. 121\u2013130. Eurographics Association (2014). https:\/\/doi.org\/10.2312\/3dor.20141058","DOI":"10.2312\/3dor.20141058"},{"key":"871_CR24","doi-asserted-by":"crossref","unstructured":"Li, Z., Xu, C., Leng, B.: Angular triplet-center loss for multi-view 3D shape retrieval. In: AAAI, pp. 8682\u20138689 (2019)","DOI":"10.1609\/aaai.v33i01.33018682"},{"key":"871_CR25","doi-asserted-by":"publisher","unstructured":"Maturana, D., Scherer, S.A.: Voxnet: A 3D convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, Hamburg, Germany, September 28-October 2, 2015, pp. 922\u2013928. IEEE (2015). https:\/\/doi.org\/10.1109\/IROS.2015.7353481","DOI":"10.1109\/IROS.2015.7353481"},{"issue":"12","key":"871_CR26","doi-asserted-by":"publisher","first-page":"16979","DOI":"10.1007\/s11042-018-7102-2","volume":"78","author":"W Nie","year":"2019","unstructured":"Nie, W., Wang, K., Wang, H., Su, Y.: The assessment of 3D model representation for retrieval with CNN\u2013RNN networks. Multimed. Tools Appl. 78(12), 16979\u201316994 (2019)","journal-title":"Multimed. Tools Appl."},{"issue":"2","key":"871_CR27","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TMM.2018.2859591","volume":"21","author":"P de Oliveira Rente","year":"2019","unstructured":"de Oliveira Rente, P., Brites, C., Ascenso, J., Pereira, F.: Graph-based static 3D point clouds geometry coding. IEEE Trans. Multimed. 21(2), 284\u2013299 (2019). https:\/\/doi.org\/10.1109\/TMM.2018.2859591","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"871_CR28","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s11263-016-0890-9","volume":"120","author":"W Ouyang","year":"2016","unstructured":"Ouyang, W., Zeng, X., Wang, X.: Learning mutual visibility relationship for pedestrian detection with a deep model. Int. J. Comput. Vis. 120(1), 14\u201327 (2016). https:\/\/doi.org\/10.1007\/s11263-016-0890-9","journal-title":"Int. J. Comput. Vis."},{"key":"871_CR29","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., K\u00f6pf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: an imperative style, high-performance deep learning library. In: H.M. Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d\u2019Alch\u00e9-Buc, E.B. Fox, R.\u00a0Garnett (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8\u201314, 2019, Vancouver, BC, Canada, pp. 8024\u20138035 (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract.html"},{"key":"871_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08234-9_279-2","volume-title":"Encyclopedia of Computer Graphics and Games","author":"E Peed","year":"2019","unstructured":"Peed, E., Lee, N.: 3D printing, history of. In: Lee, N. (ed.) Encyclopedia of Computer Graphics and Games. Springer, Berlin (2019). https:\/\/doi.org\/10.1007\/978-3-319-08234-9_279-2"},{"issue":"6","key":"871_CR31","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1145\/360825.360839","volume":"18","author":"BT Phong","year":"1975","unstructured":"Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18(6), 311\u2013317 (1975)","journal-title":"Commun. ACM"},{"key":"871_CR32","unstructured":"Qi, A., Song, Y., Xiang, T.: Semantic embedding for sketch-based 3D shape retrieval. In: British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3\u20136, 2018, p.\u00a043. BMVA Press (2018). http:\/\/bmvc2018.org\/contents\/papers\/0040.pdf"},{"key":"871_CR33","doi-asserted-by":"publisher","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, 2017, pp. 77\u201385. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.16","DOI":"10.1109\/CVPR.2017.16"},{"key":"871_CR34","doi-asserted-by":"publisher","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view CNNs for object classification on 3D data. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27\u201330, 2016, pp. 5648\u20135656. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.609","DOI":"10.1109\/CVPR.2016.609"},{"key":"871_CR35","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: I.\u00a0Guyon, U.\u00a0von Luxburg, S.\u00a0Bengio, H.M. Wallach, R.\u00a0Fergus, S.V.N. Vishwanathan, R.\u00a0Garnett (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp. 5099\u20135108 (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/d8bf84be3800d12f74d8b05e9b89836f-Abstract.html"},{"key":"871_CR36","doi-asserted-by":"publisher","unstructured":"Saavedra, J.M., Bustos, B., Schreck, T., Yoon, S.M., Scherer, M.: Sketch-based 3D model retrieval using keyshapes for global and local representation. In: M.\u00a0Spagnuolo, M.M. Bronstein, A.M. Bronstein, A.\u00a0Ferreira (eds.) 5th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2012, Cagliari, Sardinia, Italy, May 13, 2012, pp. 47\u201350. Eurographics Association (2012). https:\/\/doi.org\/10.2312\/3DOR\/3DOR12\/047-050","DOI":"10.2312\/3DOR\/3DOR12\/047-050"},{"key":"871_CR37","doi-asserted-by":"publisher","first-page":"38399","DOI":"10.1109\/ACCESS.2018.2851384","volume":"6","author":"S Saravi","year":"2018","unstructured":"Saravi, S., Joannou, D., Kalawsky, R., King, M.R.N., Marr, I.P., Hall, M., Wright, P.C.J., Ravindranath, R., Hill, A.: A systems engineering hackathon\u2014a methodology involving multiple stakeholders to progress conceptual design of a complex engineered product. IEEE Access 6, 38399\u201338410 (2018)","journal-title":"IEEE Access"},{"key":"871_CR38","doi-asserted-by":"publisher","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7\u201312, 2015, pp. 815\u2013823. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"4","key":"871_CR39","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"871_CR40","doi-asserted-by":"publisher","unstructured":"Shilane, P., Min, P., Kazhdan, M.M., Funkhouser, T.A.: The Princeton shape benchmark. In: 2004 International Conference on Shape Modeling and Applications (SMI 2004), 7\u20139 June 2004, Genova, Italy, pp. 167\u2013178. IEEE Computer Society (2004). https:\/\/doi.org\/10.1109\/SMI.2004.1314504","DOI":"10.1109\/SMI.2004.1314504"},{"key":"871_CR41","doi-asserted-by":"publisher","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7\u201313, 2015, pp. 945\u2013953. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.114","DOI":"10.1109\/ICCV.2015.114"},{"key":"871_CR42","unstructured":"Sutskever, I., Martens, J., Dahl, G.E., Hinton, G.E.: On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16\u201321 June 2013, JMLR Workshop and Conference Proceedings, vol.\u00a028, pp. 1139\u20131147. JMLR.org (2013). http:\/\/proceedings.mlr.press\/v28\/sutskever13.html"},{"key":"871_CR43","first-page":"2579","volume":"9","author":"L Van Der Maaten","year":"2008","unstructured":"Van Der Maaten, L., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132625 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"871_CR44","doi-asserted-by":"publisher","unstructured":"Wang, F., Kang, L., Li, Y.: Sketch-based 3D shape retrieval using convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7\u201312, 2015, pp. 1875\u20131883. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298797","DOI":"10.1109\/CVPR.2015.7298797"},{"key":"871_CR45","doi-asserted-by":"publisher","unstructured":"Wang, J., Yang, J., Yu, K., Lv, F., Huang, T.S., Gong, Y.: Locality-constrained linear coding for image classification. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13\u201318 June 2010, pp. 3360\u20133367. IEEE Computer Society (2010). https:\/\/doi.org\/10.1109\/CVPR.2010.5540018","DOI":"10.1109\/CVPR.2010.5540018"},{"issue":"4","key":"871_CR46","first-page":"72:1","volume":"36","author":"P Wang","year":"2017","unstructured":"Wang, P., Liu, Y., Guo, Y., Sun, C., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. 36(4), 72:1-72:11 (2017)","journal-title":"ACM Trans. Graph."},{"issue":"5","key":"871_CR47","doi-asserted-by":"publisher","first-page":"146:1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38(5), 146:1-146:12 (2019). https:\/\/doi.org\/10.1145\/3326362","journal-title":"ACM Trans. Graph."},{"key":"871_CR48","doi-asserted-by":"publisher","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: B.\u00a0Leibe, J.\u00a0Matas, N.\u00a0Sebe, M.\u00a0Welling (eds.) Computer Vision\u2014ECCV 2016\u201414th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VII, Lecture Notes in Computer Science, vol. 9911, pp. 499\u2013515. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"871_CR49","doi-asserted-by":"publisher","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D shapenets: a deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7\u201312, 2015, pp. 1912\u20131920. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298801","DOI":"10.1109\/CVPR.2015.7298801"},{"issue":"11","key":"871_CR50","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1109\/TMM.2017.2698200","volume":"19","author":"J Xie","year":"2017","unstructured":"Xie, J., Dai, G., Fang, Y.: Deep multimetric learning for shape-based 3d model retrieval. IEEE Trans. Multimed. 19(11), 2463\u20132474 (2017)","journal-title":"IEEE Trans. Multimed."},{"key":"871_CR51","doi-asserted-by":"publisher","unstructured":"Xie, J., Dai, G., Zhu, F., Fang, Y.: Learning barycentric representations of 3D shapes for sketch-based 3D shape retrieval. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, 2017, pp. 3615\u20133623. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.385","DOI":"10.1109\/CVPR.2017.385"},{"issue":"7","key":"871_CR52","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1109\/TPAMI.2016.2596722","volume":"39","author":"J Xie","year":"2017","unstructured":"Xie, J., Dai, G., Zhu, F., Wong, E.K., Fang, Y.: Deepshape: deep-learned shape descriptor for 3D shape retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1335\u20131345 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"871_CR53","doi-asserted-by":"publisher","unstructured":"Yoon, S.M., Scherer, M., Schreck, T., Kuijper, A.: Sketch-based 3D model retrieval using diffusion tensor fields of suggestive contours. In: A.D. Bimbo, S.\u00a0Chang, A.W.M. Smeulders (eds.) Proceedings of the 18th International Conference on Multimedia 2010, Firenze, Italy, October 25\u201329, 2010, pp. 193\u2013200. ACM (2010). https:\/\/doi.org\/10.1145\/1873951.1873961","DOI":"10.1145\/1873951.1873961"},{"key":"871_CR54","unstructured":"Zhu, F., Xie, J., Fang, Y.: Learning cross-domain neural networks for sketch-based 3D shape retrieval. In: D.\u00a0Schuurmans, M.P. Wellman (eds.) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12\u201317, 2016, Phoenix, Arizona, USA, pp. 3683\u20133689. AAAI Press (2016). http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/11889"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00871-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-021-00871-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00871-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T06:08:17Z","timestamp":1651644497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-021-00871-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,9]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["871"],"URL":"https:\/\/doi.org\/10.1007\/s00530-021-00871-w","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,9]]},"assertion":[{"value":"16 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2022","order":3,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}