{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:16:38Z","timestamp":1783102598353,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819699209","type":"print"},{"value":"9789819699216","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-9921-6_36","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T06:15:03Z","timestamp":1753424103000},"page":"444-457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sketch-Based CAD Standard Part Retrieval Following Mechanical Definitions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0635-9318","authenticated-orcid":false,"given":"Shengling","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-3227","authenticated-orcid":false,"given":"Jiali","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9514-9637","authenticated-orcid":false,"given":"Yue","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.imavis.2019.06.010","volume":"89","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Li, X., Liu, Y., Feng, F.: A survey on freehand sketch recognition and retrieval. Image Vis. Comput. 89, 67\u201387 (2019)","journal-title":"Image Vis. Comput."},{"key":"36_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cag.2020.05.018","volume":"89","author":"M Chen","year":"2020","unstructured":"Chen, M., Wang, C., Liu, L.: Cross-domain retrieving sketch and shape using cycle CNNs. Comput. Graph. 89, 50\u201358 (2020)","journal-title":"Comput. Graph."},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Lin, H., Fu, Y., Lu, P., Gong, S., Xue, X., Jiang, Y. G.: TC-Net for iSBIR: triplet classification network for instance-level sketch based image retrieval. In Proceedings of the 27th ACM International Conference on Multimedia, pp. 1676\u20131684 (2019)","DOI":"10.1145\/3343031.3350900"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Song, Y., Zheng, Y., Zhao, S., Liu, S., Zhuang, X., Long, Z., et al.: A lightweight and effective multi-view knowledge distillation framework for text-image retrieval. In: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2024)","DOI":"10.1109\/IJCNN60899.2024.10650723"},{"key":"36_CR5","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1162\/tacl_a_00473","volume":"10","author":"G Geigle","year":"2022","unstructured":"Geigle, G., Pfeiffer, J., Reimers, N., Vuli\u0107, I., Gurevych, I.: Retrieve fast, rerank smart: cooperative and joint approaches for improved cross-modal retrieval. Trans. Assoc. Comput. Linguist. 10, 503\u2013521 (2022)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Cao, B., Araujo, A., Sim, J.: Unifying deep local and global features for image search. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, pp. 726\u2013743 (2020)","DOI":"10.1007\/978-3-030-58565-5_43"},{"key":"36_CR7","unstructured":"Lu, J., Hall, K., Ma, J., Ni, J.: HYRR: hybrid infused reranking for passage retrieval. In International Conference on Language Resources and Evaluation (2022)"},{"key":"36_CR8","first-page":"1","volume":"62","author":"J Huang","year":"2024","unstructured":"Huang, J., Chen, Y., Xiong, S., Lu, X.: Cross-modal remote sensing image-audio retrieval with adaptive learning for aligning correlation. IEEE Trans. Geosci. Remote Sens. 62, 1\u201313 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"36_CR9","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s11263-016-0932-3","volume":"122","author":"Q Yu","year":"2017","unstructured":"Yu, Q., Yang, Y., Liu, F., Song, Y.Z., Xiang, T., Hospedales, T.M.: Sketch-a-net: a deep neural network that beats humans. Int. J. Comput. Vision 122, 411\u2013425 (2017)","journal-title":"Int. J. Comput. Vision"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., Chowdhury, P.N., Sain, A., Yang, Y., Xiang, T., Song, Y.Z.: More photos are all you need: semi-supervised learning for fine-grained sketch based image retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4247\u20134256 (2021)","DOI":"10.1109\/CVPR46437.2021.00423"},{"issue":"11","key":"36_CR11","first-page":"2950","volume":"22","author":"Y Xu","year":"2020","unstructured":"Xu, Y., Hu, J., Wattanachote, K., Zeng, K., Gong, Y.: Sketch-based shape retrieval via best view selection and a cross-domain similarity measure. IEEE Trans. Multimedia 22(11), 2950\u20132962 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"36_CR12","doi-asserted-by":"publisher","first-page":"142632","DOI":"10.1109\/ACCESS.2020.3013595","volume":"8","author":"W Nie","year":"2020","unstructured":"Nie, W., Wang, Y., Song, D., Li, W.: 3D model retrieval based on a 3D shape knowledge graph. IEEE Access 8, 142632\u2013142641 (2020)","journal-title":"IEEE Access"},{"key":"36_CR13","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.ins.2018.09.051","volume":"474","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., Zeng, F., Qian, J., Han, X.: 3D shape classification and retrieval based on polar view. Inf. Sci. 474, 205\u2013220 (2019)","journal-title":"Inf. Sci."},{"key":"36_CR14","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.cag.2022.07.009","volume":"107","author":"J Qin","year":"2022","unstructured":"Qin, J., Yuan, S., Chen, J., Amor, B.B., Fang, Y., Hoang-Xuan, N., et al.: Shrec\u201922 track: sketch-based 3D shape retrieval in the wild. Comput. Graph. 107, 104\u2013115 (2022)","journal-title":"Comput. Graph."},{"key":"36_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102008","volume":"56","author":"J Hou","year":"2023","unstructured":"Hou, J., Luo, C., Qin, F., Shao, Y., Chen, X.: FuS-GCN: efficient b-rep based graph convolutional networks for 3D-CAD model classification and retrieval. Adv. Eng. Inform. 56, 102008 (2023)","journal-title":"Adv. Eng. Inform."},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: 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":"36_CR17","doi-asserted-by":"crossref","unstructured":"He, X., Zhou, Y., Zhou, Z., Bai, S., Bai, X.: Triplet-center Loss for Multi-view 3D object retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1945\u20131954 (2018)","DOI":"10.1109\/CVPR.2018.00208"},{"issue":"4","key":"36_CR18","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.3390\/s20041188","volume":"20","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al.: Training convolutional neural networks with multi-size images and triplet loss for remote sensing scene classification. Sensors 20(4), 1188 (2020)","journal-title":"Sensors"},{"issue":"1","key":"36_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40708-023-00184-w","volume":"10","author":"F Hajamohideen","year":"2023","unstructured":"Hajamohideen, F., Shaffi, N., Mahmud, M., Subramanian, K., Al Sariri, A., Vimbi, V., et al.: Four-way classification of Alzheimer\u2019s disease using deep Siamese convolutional neural network with triplet-loss function. Brain Inf. 10(1), 5 (2023)","journal-title":"Brain Inf."},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Xuan, H., Stylianou, A., Liu, X., Pless, R.: Hard negative examples are hard, but useful. In: Computer Vision ECCV 2020: 16th European Conference, Glasgow, UK, pp. 126\u2013142 (2020)","DOI":"10.1007\/978-3-030-58568-6_8"},{"key":"36_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101427","volume":"51","author":"F Qin","year":"2022","unstructured":"Qin, F., Qiu, S., Gao, S., Bai, J.: 3D CAD model retrieval based on sketch and unsupervised variational autoencoder. Adv. Eng. Inform. 51, 101427 (2022)","journal-title":"Adv. Eng. Inform."},{"key":"36_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103903","volume":"239","author":"S Liang","year":"2024","unstructured":"Liang, S., Dai, W., Cai, Y., Xie, C.: Sketch-based 3D shape retrieval via teacher-student learning. Comput. Vis. Image Underst. 239, 103903 (2024)","journal-title":"Comput. Vis. Image Underst."},{"issue":"9","key":"36_CR23","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/j.cad.2006.06.007","volume":"38","author":"S Jayanti","year":"2006","unstructured":"Jayanti, S., Kalyanaraman, Y., Iyer, N., Ramani, K.: Developing an engineering shape benchmark for CAD models. Comput. Aided Des. 38(9), 939\u2013953 (2006)","journal-title":"Comput. Aided Des."},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.cag.2021.07.001","volume":"99","author":"B Manda","year":"2021","unstructured":"Manda, B., Dhayarkar, S., Mitheran, S., Viekash, V.K., Muthuganapathy, R.: \u2018CADSketchNet\u2019-an annotated sketch dataset for 3D CAD model retrieval with deep neural networks. Comput. Graph. 99, 100\u2013113 (2021)","journal-title":"Comput. Graph."},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Z., 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)","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"36_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cviu.2017.06.007","volume":"164","author":"T Bui","year":"2017","unstructured":"Bui, T., Ribeiro, L., Ponti, M., Collomosse, J.: Compact descriptors for sketch-based image retrieval using a triplet loss convolutional neural network. Comput. Vis. Image Underst. 164, 27\u201337 (2017)","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"36_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459818","volume":"40","author":"KD Willis","year":"2021","unstructured":"Willis, K.D., Pu, Y., Luo, J., Chu, H., Du, T., Lambourne, J.G., et al.: Fusion 360 gallery: a dataset and environment for programmatic CAD construction from human design sequences. ACM Trans. Graph. (TOG) 40(4), 1\u201324 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"36_CR28","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.cag.2022.07.006","volume":"107","author":"B Manda","year":"2022","unstructured":"Manda, B., Kendre, P.P., Dey, S., Muthuganapathy, R.: SketchCleanNet\u2013a deep learning approach to the enhancement and correction of query sketches for a 3D CAD model retrieval system. Comput. Graph. 107, 73\u201383 (2022)","journal-title":"Comput. Graph."},{"issue":"4","key":"36_CR29","doi-asserted-by":"publisher","DOI":"10.1115\/1.4043211","volume":"19","author":"A Angrish","year":"2018","unstructured":"Angrish, A., Craver, B., Starly, B.: \u201cFabSearch\u201d: a 3D CAD model-based search engine for sourcing manufacturing services. J. Comput. Inf. Sci. Eng. 19(4), 041006 (2018)","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Jayaraman, P.K., et al.: UV-Net: learning from boundary representations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11703\u201311712 (2021)","DOI":"10.1109\/CVPR46437.2021.01153"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9921-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:35:23Z","timestamp":1783100123000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9921-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819699209","9789819699216"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9921-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}