{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T19:10:08Z","timestamp":1745608208864,"version":"3.40.4"},"publisher-location":"Singapore","reference-count":51,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819658114","type":"print"},{"value":"9789819658121","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\/978-981-96-5812-1_11","type":"book-chapter","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:44:37Z","timestamp":1745606677000},"page":"194-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Accuracy Fractured Object Reassembly Under Arbitrary Poses"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5954-4699","authenticated-orcid":false,"given":"Qun-Ce","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-4374","authenticated-orcid":false,"given":"Yan-Pei","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5058-8856","authenticated-orcid":false,"given":"Weihao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9197-346X","authenticated-orcid":false,"given":"Tai-Jiang","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7673-8325","authenticated-orcid":false,"given":"Ying","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8071-5756","authenticated-orcid":false,"given":"Yong-Liang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-6542","authenticated-orcid":false,"given":"Shi-Min","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"issue":"3","key":"11_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1360612.1360684","volume":"27","author":"D Aiger","year":"2008","unstructured":"Aiger, D., Mitra, N.J., Cohen-Or, D.: 4-points congruent sets for robust pairwise surface registration. ACM Trans. Graph. 27(3), 1\u201310 (2008)","journal-title":"ACM Trans. Graph."},{"key":"11_CR2","unstructured":"Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)"},{"issue":"2","key":"11_CR3","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"P Besl","year":"1992","unstructured":"Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239\u2013256 (1992)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"11_CR4","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1111\/cgf.12178","volume":"32","author":"S Bouaziz","year":"2013","unstructured":"Bouaziz, S., Tagliasacchi, A., Pauly, M.: Sparse iterative closest point. Comput. Graph. Forum 32(5), 113\u2013123 (2013)","journal-title":"Comput. Graph. Forum"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Brachmann, E., et al.: DSAC-differentiable RANSAC for camera localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6684\u20136692 (2017)","DOI":"10.1109\/CVPR.2017.267"},{"issue":"3","key":"11_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1360612.1360683","volume":"27","author":"BJ Brown","year":"2008","unstructured":"Brown, B.J., et al.: A system for high-volume acquisition and matching of fresco fragments: reassembling theran wall paintings. ACM Trans. Graph. (TOG) 27(3), 1\u20139 (2008)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"11_CR7","unstructured":"Chang, A.X., et al.: Shapenet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"11_CR8","unstructured":"Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. In: Proceedings of 1991 IEEE International Conference on Robotics and Automation, vol. 3, pp. 2724\u20132729 (1991)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Y.C., Li, H., Turpin, D., Jacobson, A., Garg, A.: Neural shape mating: self-supervised object assembly with adversarial shape priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12724\u201312733 (2022)","DOI":"10.1109\/CVPR52688.2022.01239"},{"issue":"6","key":"11_CR10","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM"},{"key":"11_CR11","unstructured":"Gelfand, N., Ikemoto, L., Rusinkiewicz, S., Levoy, M.: Geometrically stable sampling for the ICP algorithm. In: Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003, Proceedings, pp. 260\u2013267 (2003)"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Desbrun, M., Pottmann, H. (eds.) Third Eurographics Symposium on Geometry Processing, Vienna, Austria, 4\u20136 July 2005. ACM International Conference Proceeding Series, vol.\u00a0255, pp. 197\u2013206. Eurographics Association (2005). https:\/\/doi.org\/10.2312\/SGP\/SGP05\/197-206","DOI":"10.2312\/SGP\/SGP05\/197-206"},{"key":"11_CR13","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"issue":"4","key":"11_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530078","volume":"41","author":"H Guo","year":"2022","unstructured":"Guo, H., Liu, S., Pan, H., Liu, Y., Tong, X., Guo, B.: Complexgen: cad reconstruction by b-rep chain complex generation. ACM Trans. Graph. (TOG) 41(4), 1\u201318 (2022)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"12","key":"11_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-3097-4","volume":"63","author":"S-M Hu","year":"2020","unstructured":"Hu, S.-M., Liang, D., Yang, G.-Y., Yang, G.-W., Zhou, W.-Y.: Jittor: a novel deep learning framework with meta-operators and unified graph execution. Sci. China Inf. Sci. 63(12), 1\u201321 (2020). https:\/\/doi.org\/10.1007\/s11432-020-3097-4","journal-title":"Sci. China Inf. Sci."},{"key":"11_CR16","doi-asserted-by":"publisher","unstructured":"Huang, H., Gong, M., Cohen-Or, D., Ouyang, Y., Tan, F., Zhang, H.: Field-guided registration for feature-conforming shape composition. ACM Trans. Graph. 31(6) (2012). https:\/\/doi.org\/10.1145\/2366145.2366198","DOI":"10.1145\/2366145.2366198"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Huang, Q., Fl\u00f6ry, S., Gelfand, N., Hofer, M., Pottmann, H.: Reassembling fractured objects by geometric matching. ACM Trans. Graph. 25(3), 569\u2013578 (2006). https:\/\/doi.org\/10.1145\/1141911.1141925","DOI":"10.1145\/1141911.1141925"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., Schindler, K.: Predator: registration of 3D point clouds with low overlap. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4267\u20134276 (2021)","DOI":"10.1109\/CVPR46437.2021.00425"},{"issue":"5","key":"11_CR19","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/34.765655","volume":"21","author":"A Johnson","year":"1999","unstructured":"Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433\u2013449 (1999). https:\/\/doi.org\/10.1109\/34.765655","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478513.3480562","volume":"40","author":"B Jones","year":"2021","unstructured":"Jones, B., Hildreth, D., Chen, D., Baran, I., Kim, V.G., Schulz, A.: Automate: a dataset and learning approach for automatic mating of cad assemblies. ACM Trans. Graph. (TOG) 40(6), 1\u201318 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"11_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"11_CR22","unstructured":"Koller, D., Levoy, M.: Computer-aided reconstruction and new matches in the forma urbis romae. In: Computer-Aided Reconstruction and New Matches in The Forma Urbis Romae, pp. 103\u2013125 (2006)"},{"issue":"6","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550454.3555470","volume":"41","author":"N Lamb","year":"2022","unstructured":"Lamb, N., Banerjee, S., Banerjee, N.K.: Deepjoin: learning a joint occupancy, signed distance, and normal field function for shape repair. ACM Trans. Graph. (TOG) 41(6), 1\u201310 (2022)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Lamb, N., Banerjee, S., Banerjee, N.K.: Deepmend: learning occupancy functions to represent shape for repair. In: Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part III, pp. 433\u2013450. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-20062-5_25"},{"key":"11_CR25","doi-asserted-by":"publisher","unstructured":"Lamb, N., Banerjee, S., Banerjee, N.K.: Mendnet: restoration of fractured shapes using learned occupancy functions. Comput. Graph. Forum 41(5), 65\u201378 (2022). https:\/\/doi.org\/10.1111\/CGF.14603","DOI":"10.1111\/CGF.14603"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Levoy, M., Rusinkiewicz, S.: Efficient variants of the ICP algorithm. In: International Conference on 3D Digital Imaging and Modeling, p. 145 (2001)","DOI":"10.1109\/IM.2001.924423"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Liao, S.H., Xiong, C., Liu, S., Zhang, Y.Q., Peng, C.L.: 3D object reassembly using region-pair-relation and balanced cluster tree. Comput. Methods Programs Biomed. 197, 105756 (2020)","DOI":"10.1016\/j.cmpb.2020.105756"},{"key":"11_CR28","unstructured":"Lu, J., Sun, Y., Huang, Q.: Jigsaw: learning to assemble multiple fractured objects. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, 10\u201316 December 2023 (2023). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/30ae2af8612ac74357363e8ae877d80c-Abstract-Conference.html"},{"issue":"5","key":"11_CR29","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1111\/cgf.12446","volume":"33","author":"N Mellado","year":"2014","unstructured":"Mellado, N., Aiger, D., Mitra, N.J.: Super 4pcs fast global pointcloud registration via smart indexing. Comput. Graph. Forum 33(5), 205\u2013215 (2014)","journal-title":"Comput. Graph. Forum"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Mo, K., et al.: Partnet: a large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 909\u2013918 (2019)","DOI":"10.1109\/CVPR.2019.00100"},{"key":"11_CR31","unstructured":"Narayan, A., Nagar, R., Raman, S.: RGL-net: a recurrent graph learning framework for progressive part assembly. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 78\u201387 (2022)"},{"issue":"2","key":"11_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/38.909015","volume":"21","author":"G Papaioannou","year":"2001","unstructured":"Papaioannou, G., Karabassi, E.A., Theoharis, T.: Virtual archaeologist: assembling the past. IEEE Comput. Graphics Appl. 21(2), 53\u201359 (2001)","journal-title":"IEEE Comput. Graphics Appl."},{"issue":"5","key":"11_CR33","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/S0262-8856(03)00008-8","volume":"21","author":"G Papaioannou","year":"2003","unstructured":"Papaioannou, G., Karabassi, E.A.: On the automatic assemblage of arbitrary broken solid artefacts. Image Vis. Comput. 21(5), 401\u2013412 (2003)","journal-title":"Image Vis. Comput."},{"issue":"2","key":"11_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3009905","volume":"10","author":"G Papaioannou","year":"2017","unstructured":"Papaioannou, G., et al.: From reassembly to object completion: a complete systems pipeline. J. Comput. Cult. Heritage (JOCCH) 10(2), 1\u201322 (2017)","journal-title":"J. Comput. Cult. Heritage (JOCCH)"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Raposo, C., Barreto, J.P.: Using 2 point+normal sets for fast registration of point clouds with small overlap. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5652\u20135658 (2017)","DOI":"10.1109\/ICRA.2017.7989664"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 3384\u20133391 (2008)","DOI":"10.1109\/IROS.2008.4650967"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: Superglue: learning feature matching with graph neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4938\u20134947 (2020)","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"11_CR38","doi-asserted-by":"publisher","unstructured":"Scarpellini, G., Fiorini, S., Giuliari, F., Morerio, P., Bue, A.D.: Diffassemble: a unified graph-diffusion model for 2D and 3D reassembly. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, 16\u201322 June 2024, pp. 28098\u201328108. IEEE (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02654","DOI":"10.1109\/CVPR52733.2024.02654"},{"key":"11_CR39","unstructured":"Sell\u00e1n, S., Chen, Y.C., Wu, Z., Garg, A., Jacobson, A.: Breaking bad: a dataset for geometric fracture and reassembly. In: Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Sell\u00e1n, S., Luong, J., Silva, L.M.D., Ramakrishnan, A., Yang, Y., Jacobson, A.: Breaking good: fracture modes for realtime destruction. ACM Trans. Graph. (2022)","DOI":"10.1145\/3549540"},{"issue":"7","key":"11_CR41","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/TVCG.2012.310","volume":"19","author":"GK Tam","year":"2013","unstructured":"Tam, G.K., et al.: Registration of 3D point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans. Visual Comput. Graphics 19(7), 1199\u20131217 (2013)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"11_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-642-15558-1_26","volume-title":"Computer Vision \u2013 ECCV 2010","author":"F Tombari","year":"2010","unstructured":"Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356\u2013369. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15558-1_26"},{"key":"11_CR43","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Solomon, J.M.: Deep closest point: learning representations for point cloud registration. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3523\u20133532 (2019)","DOI":"10.1109\/ICCV.2019.00362"},{"issue":"5","key":"11_CR45","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., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Willis, K.D., et al.: Joinable: learning bottom-up assembly of parametric cad joints. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15849\u201315860 (2022)","DOI":"10.1109\/CVPR52688.2022.01539"},{"key":"11_CR47","doi-asserted-by":"publisher","unstructured":"Wu, R., Tie, C., Du, Y., Zhao, Y., Dong, H.: Leveraging SE(3) equivariance for learning 3d geometric shape assembly. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, 1\u20136 October 2023, pp. 14265\u201314274. IEEE (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01316","DOI":"10.1109\/ICCV51070.2023.01316"},{"key":"11_CR48","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)"},{"issue":"2","key":"11_CR49","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s41095-020-0174-8","volume":"6","author":"Y-P Xiao","year":"2020","unstructured":"Xiao, Y.-P., Lai, Y.-K., Zhang, F.-L., Li, C., Gao, L.: A survey on deep geometry learning: from a representation perspective. Comput. Visual Media 6(2), 113\u2013133 (2020). https:\/\/doi.org\/10.1007\/s41095-020-0174-8","journal-title":"Comput. Visual Media"},{"issue":"12","key":"11_CR50","doi-asserted-by":"publisher","first-page":"4304","DOI":"10.1109\/TVCG.2021.3086113","volume":"28","author":"Z Yan","year":"2021","unstructured":"Yan, Z., Yi, Z., Hu, R., Mitra, N.J., Cohen-Or, D., Huang, H.: Consistent two-flow network for tele-registration of point clouds. IEEE Trans. Visual Comput. Graphics 28(12), 4304\u20134318 (2021)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"11_CR51","first-page":"6315","volume":"33","author":"G Zhan","year":"2020","unstructured":"Zhan, G., et al.: Generative 3D part assembly via dynamic graph learning. Adv. Neural. Inf. Process. Syst. 33, 6315\u20136326 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Computational Visual Media"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-5812-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:44:52Z","timestamp":1745606692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-5812-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819658114","9789819658121"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-5812-1_11","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 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Visual Media","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong SAR","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":"19 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccvm.org\/2025\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}