{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:39:49Z","timestamp":1783021189265,"version":"3.54.6"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045546","type":"print"},{"value":"9783032045553","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04555-3_20","type":"book-chapter","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T08:55:42Z","timestamp":1757580942000},"page":"239-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uniform Representation of\u00a0Parametric CAD Models for\u00a0Generative Application"],"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":[[2025,9,12]]},"reference":[{"key":"20_CR1","unstructured":"Alam, M.F., Ahmed, F.: Gencad: Image-conditioned computer-aided design generation with transformer-based contrastive representation and diffusion priors. arXiv preprint arXiv:2409.16294 (2024)"},{"key":"20_CR2","doi-asserted-by":"crossref","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 (2018)","DOI":"10.1115\/1.4043211"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., 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), pp. 77\u201385 (2017)","DOI":"10.1109\/CVPR.2017.16"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Dupont, E., et al.: Cadops-net: jointly learning cad operation types and steps from boundary-representations. In: 2022 International Conference on 3D Vision (3DV), pp. 114\u2013123 (2022)","DOI":"10.1109\/3DV57658.2022.00024"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Dupont, E., Cherenkova, K., Mallis, D., Gusev, G., Kacem, A., Aouada, D.: Transcad: a hierarchical transformer for cad sequence inference from point clouds. In: European Conference on Computer Vision, pp. 19\u201336. Springer (2024)","DOI":"10.1007\/978-3-031-73030-6_2"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Guo, H., Liu, S., Pan, H., Liu, Y., Tong, X., Guo, B.: Complexgen: cad reconstruction by b-rep chain complex generation 41(4) (2022)","DOI":"10.1145\/3528223.3530078"},{"key":"20_CR7","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"20_CR8","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":"20_CR9","doi-asserted-by":"crossref","unstructured":"Jayaraman, P.K., Sanghi, A., Lambourne, J., Davies, T., Shayani, H.: UV-net: Learning from boundary representations. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11698\u201311707 (2021)","DOI":"10.1109\/CVPR46437.2021.01153"},{"key":"20_CR10","unstructured":"Kania, K., Ziundefinedba, M., Kajdanowicz, T.: UCSG-net - unsupervised discovering of constructive solid geometry tree. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS 2020, Curran Associates Inc., Red Hook (2020)"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Kesler, S., Slama, D.: Grastnet: Graph neural networks and set transformer for machining feature recognition. In: 2024 International Conference on Control, Automation and Diagnosis (ICCAD), pp.\u00a01\u20136 (2024)","DOI":"10.1109\/ICCAD60883.2024.10553906"},{"key":"20_CR12","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Koch, S., et al.: ABC: a big cad model dataset for geometric deep learning. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9593\u20139603 (2019)","DOI":"10.1109\/CVPR.2019.00983"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Lambourne, J., Willis, K.D.D., Jayaraman, P.K., Sanghi, A., Meltzer, P., Shayani, H.: BRepNet: a topological message passing system for solid models. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12768\u201312777 (2021)","DOI":"10.1109\/CVPR46437.2021.01258"},{"key":"20_CR15","first-page":"2384","volume":"10","author":"J Lee","year":"2023","unstructured":"Lee, J., Yeo, C., Cheon, S.U., Park, J.H., Mun, D.: Brepgat: graph neural network to segment machining feature faces in a b-rep model. J. Comput. Des. Eng. 10, 2384\u20132400 (2023)","journal-title":"J. Comput. Des. Eng."},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Lee, K.: Partial entity structure: a compact non-manifold boundary representation based on partial topological entities. In: International Conference on Smart Media and Applications (2001)","DOI":"10.1145\/376957.376976"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Li, C., Pan, H., Bousseau, A., Mitra, N.J.: Free2cad: parsing freehand drawings into cad commands. ACM Trans. Graph. 41(4) (2022)","DOI":"10.1145\/3528223.3530133"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Li, P., Guo, J., Zhang, X., Yan, D.M.: SECAD-Net: self-supervised cad reconstruction by learning sketch-extrude operations. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16816\u201316826 (2023)","DOI":"10.1109\/CVPR52729.2023.01613"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Li, X., Ye, T., Shan, C., Li, D., Gao, M.: Seegera: self-supervised semi-implicit graph variational auto-encoders with masking. In: Proceedings of the ACM Web Conference 2023, WWW 2023, pp. 143\u2013153. Association for Computing Machinery, New York (2023)","DOI":"10.1145\/3543507.3583245"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Lou, Y., Li, X., Chen, H., Zhou, X.: BREP-BERT: pre-training boundary representation BERT with sub-graph node contrastive learning. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (2023)","DOI":"10.1145\/3583780.3614795"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 (2018)","DOI":"10.24963\/ijcai.2018\/362"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Sanghi, A., et al.: Clip-forge: towards zero-shot text-to-shape generation. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18582\u201318592 (2022)","DOI":"10.1109\/CVPR52688.2022.01805"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Sharma, G., Goyal, R., Liu, D., Kalogerakis, E., Maji, S.: CSGNET: neural shape parser for constructive solid geometry. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5515\u20135523 (2017)","DOI":"10.1109\/CVPR.2018.00578"},{"key":"20_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110920","volume":"279","author":"C Sun","year":"2023","unstructured":"Sun, C.: Hat-GAE: self-supervised graph auto-encoders with hierarchical adaptive masking and trainable corruption. Knowl. Based Syst. 279, 110920 (2023)","journal-title":"Knowl. Based Syst."},{"key":"20_CR25","unstructured":"Tang, J., et al.: Edgerunner: auto-regressive auto-encoder for artistic mesh generation. arXiv preprint arXiv:2409.18114 (2024)"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: HGATE: heterogeneous graph attention auto-encoders. IEEE Trans. Knowl. Data Eng. 35(4), 3938\u20133951 (2023)","DOI":"10.1109\/TKDE.2021.3138788"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds 38(5) (2019)","DOI":"10.1145\/3326362"},{"key":"20_CR28","unstructured":"Weiler, K.J.: Topological structures for geometric modeling. PHD Thesis Rensselelaer Polytechnic Institute (1986)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Xu, X., Lambourne, J.G., Jayaraman, P.K., Wang, Z., Willis, K.D., Furukawa, Y.: Brepgen: a B-REP generative diffusion model with structured latent geometry. arXiv preprint arXiv:2401.15563 (2024)","DOI":"10.1145\/3658129"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"You, Y., et al.: Img2CAD: reverse engineering 3d cad models from images through vlm-assisted conditional factorization. arXiv preprint arXiv:2408.01437 (2024)","DOI":"10.1145\/3757377.3763891"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04555-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:43:58Z","timestamp":1783017838000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04555-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"ISBN":["9783032045546","9783032045553"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04555-3_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","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":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}