{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T04:01:50Z","timestamp":1747454510735,"version":"3.40.5"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031889769","type":"print"},{"value":"9783031889776","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\/978-3-031-88977-6_20","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T05:22:47Z","timestamp":1747372967000},"page":"216-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Point Transformers for\u00a0Detecting Anatomical Landmarks in\u00a0Digital Dentistry"],"prefix":"10.1007","author":[{"given":"Tibor","family":"Kub\u00edk","sequence":"first","affiliation":[]},{"given":"Old\u0159ich","family":"Kodym","sequence":"additional","affiliation":[]},{"given":"Petr","family":"\u0160illing","sequence":"additional","affiliation":[]},{"given":"Kate\u0159ina","family":"Tr\u00e1vn\u00ed\u010dkov\u00e1","sequence":"additional","affiliation":[]},{"given":"Tom\u00e1\u0161","family":"Moj\u017ei\u0161","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Matula","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,17]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Ahn, H.J., et al.: A comparative analysis of artificial intelligence and manual methods for three-dimensional anatomical landmark identification in dentofacial treatment planning. Bioengineering 11(4) (2024). https:\/\/doi.org\/10.3390\/bioengineering11040318, https:\/\/www.mdpi.com\/2306-5354\/11\/4\/318","DOI":"10.3390\/bioengineering11040318"},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Ben-Hamadou, A., et al.: 3DTeethLand: 3D teeth landmarks detection challenge (2024). https:\/\/doi.org\/10.5281\/zenodo.10991302","DOI":"10.5281\/zenodo.10991302"},{"key":"20_CR3","unstructured":"Ben-Hamadou, A., et\u00a0al.: Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans . arXiv preprint arXiv:2210.06094 (2022)"},{"key":"20_CR4","unstructured":"Ben-Hamadou, A., et al.: 3DTeethseg\u201922: 3D teeth scan segmentation and labeling challenge. arXiv preprint arXiv:2305.18277 (2023)"},{"key":"20_CR5","unstructured":"Bronstein, M.M., Bruna, J., Cohen, T., Veli\u010dkovi\u0107, P.: Geometric deep learning: grids, groups, graphs, geodesics, and gauges (2021). https:\/\/arxiv.org\/abs\/2104.13478"},{"key":"20_CR6","doi-asserted-by":"publisher","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6526\u20136534 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.691","DOI":"10.1109\/CVPR.2017.691"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, J., Zhang, X., Qi, X., Jia, J.: VoxelNeXt: fully sparse VoxelNet for 3D object detection and tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21674\u201321683 (2023)","DOI":"10.1109\/CVPR52729.2023.02076"},{"key":"20_CR8","unstructured":"Crane, K., Livesu, M., Puppo, E., Qin, Y.: A survey of algorithms for geodesic paths and distances (2020). https:\/\/arxiv.org\/abs\/2007.10430"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Q., et al.: Laplacian2mesh: laplacian-based mesh understanding. IEEE Trans. Visual. Comput. Graph. (2023)","DOI":"10.1109\/TVCG.2023.3259044"},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Ham, G.S., Oh, K.: Learning spatial configuration feature for landmark localization in hand x-rays. Electronics 12(19) (2023). https:\/\/doi.org\/10.3390\/electronics12194038, https:\/\/www.mdpi.com\/2079-9292\/12\/19\/4038","DOI":"10.3390\/electronics12194038"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: MeshCNN: a network with an edge. 38(4) (2019). https:\/\/doi.org\/10.1145\/3306346.3322959","DOI":"10.1145\/3306346.3322959"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Hu, S.M., et al.: Subdivision-based mesh convolution networks 41(3) (2022). https:\/\/doi.org\/10.1145\/3506694","DOI":"10.1145\/3506694"},{"key":"20_CR13","unstructured":"Khalid, M.A., et al.: Cepha29: automatic cephalometric landmark detection challenge 2023 (2023). https:\/\/arxiv.org\/abs\/2212.04808"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Kub\u00edk, T., Spanel, M.: Robust teeth detection in 3D dental scans by automated multi-view landmarking. In: Bioimaging (Bristol. Print) (2022). https:\/\/api.semanticscholar.org\/CorpusID:247241815","DOI":"10.5220\/0010770700003123"},{"key":"20_CR15","doi-asserted-by":"publisher","unstructured":"Le, T., Bui, G., Duan, Y.: A multi-view recurrent neural network for 3D mesh segmentation. Comput. Graphics 66 (2017). https:\/\/doi.org\/10.1016\/j.cag.2017.05.011","DOI":"10.1016\/j.cag.2017.05.011"},{"key":"20_CR16","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on $$\\cal{X}$$-transformed points (2018). https:\/\/arxiv.org\/abs\/1801.07791"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Liang, Y., Zhao, S., Yu, B., Zhang, J., He, F.: MeshMAE: masked autoencoders for 3D mesh data analysis (2022). https:\/\/arxiv.org\/abs\/2207.10228","DOI":"10.1007\/978-3-031-20062-5_3"},{"key":"20_CR18","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019). https:\/\/arxiv.org\/abs\/1711.05101"},{"key":"20_CR19","doi-asserted-by":"publisher","unstructured":"Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922\u2013928 (2015). https:\/\/doi.org\/10.1109\/IROS.2015.7353481","DOI":"10.1109\/IROS.2015.7353481"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Mokhtari, M., et al.: EchoGLAD: hierarchical graph neural networks for left ventricle landmark detection on echocardiograms (2023). https:\/\/arxiv.org\/abs\/2307.12229","DOI":"10.1007\/978-3-031-43901-8_22"},{"key":"20_CR21","doi-asserted-by":"publisher","first-page":"21541","DOI":"10.1109\/ACCESS.2022.3151350","volume":"10","author":"A Mukhaimar","year":"2022","unstructured":"Mukhaimar, A., Tennakoon, R., Lai, C.Y., Hoseinnezhad, R., Bab-Hadiashar, A.: Robust object classification approach using spherical harmonics. IEEE Access 10, 21541\u201321553 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3151350","journal-title":"IEEE Access"},{"key":"20_CR22","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, 21\u201326 July 2017, pp. 77\u201385. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.16","DOI":"10.1109\/CVPR.2017.16"},{"key":"20_CR23","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems 30: annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017, Long Beach, CA, USA, pp. 5099\u20135108 (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/d8bf84be3800d12f74d8b05e9b89836f-Abstract.html"},{"key":"20_CR24","doi-asserted-by":"publisher","unstructured":"Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. In: Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1986, pp. 151\u2013160. Association for Computing Machinery, New York (1986). https:\/\/doi.org\/10.1145\/15922.15903","DOI":"10.1145\/15922.15903"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Sharp, N., Attaiki, S., Crane, K., Ovsjanikov, M.: DiffusionNet: discretization agnostic learning on surfaces (2022). https:\/\/arxiv.org\/abs\/2012.00888","DOI":"10.1145\/3507905"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition (2015). https:\/\/arxiv.org\/abs\/1505.00880","DOI":"10.1109\/ICCV.2015.114"},{"key":"20_CR27","doi-asserted-by":"publisher","unstructured":"Tezzele, M., Demo, N., Mola, A., Rozza, G.: PyGeM: python geometrical morphing. Softw. Impacts 100047 (2020). https:\/\/doi.org\/10.1016\/j.simpa.2020.100047, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S2665963820300385","DOI":"10.1016\/j.simpa.2020.100047"},{"key":"20_CR28","doi-asserted-by":"publisher","unstructured":"Triarjo, S., Sarno, R., Hidayati, S.C., Sihaj, G.: Automatic 3D digital dental landmark based on point transformation weight. In: 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 336\u2013341 (2023). https:\/\/doi.org\/10.1109\/ICAIIC57133.2023.10067081","DOI":"10.1109\/ICAIIC57133.2023.10067081"},{"key":"20_CR29","doi-asserted-by":"publisher","unstructured":"Wang, P.S.: OctFormer: octree-based transformers for 3D point clouds. ACM Trans. Graph. 42(4) (2023). https:\/\/doi.org\/10.1145\/3592131","DOI":"10.1145\/3592131"},{"key":"20_CR30","doi-asserted-by":"publisher","unstructured":"Wei, G., et al.: Dense representative tooth landmark\/axis detection network on 3D model. Comput. Aided Geom. Des. 94, 102077 (2022). https:\/\/doi.org\/10.1016\/j.cagd.2022.102077, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167839622000139","DOI":"10.1016\/j.cagd.2022.102077"},{"key":"20_CR31","doi-asserted-by":"publisher","unstructured":"Woodsend, B., Koufoudaki, E., Mossey, P.A., Lin, P.: Automatic recognition of landmarks on digital dental models. Comput. Biol. Med. 137, 104819 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104819, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0010482521006132","DOI":"10.1016\/j.compbiomed.2021.104819"},{"issue":"11","key":"20_CR32","doi-asserted-by":"publisher","first-page":"3158","DOI":"10.1109\/TMI.2022.3180343","volume":"41","author":"TH Wu","year":"2022","unstructured":"Wu, T.H., et al.: Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE Trans. Med. Imaging 41(11), 3158\u20133166 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3180343","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds (2020). https:\/\/arxiv.org\/abs\/1811.07246","DOI":"10.1109\/CVPR.2019.00985"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Wu, X., et al.: Point transformer v3: simpler, faster, stronger (2024). https:\/\/arxiv.org\/abs\/2312.10035","DOI":"10.1109\/CVPR52733.2024.00463"},{"key":"20_CR35","unstructured":"Wu, X., Lao, Y., Jiang, L., Liu, X., Zhao, H.: Point transformer v2: grouped vector attention and partition-based pooling (2022). https:\/\/arxiv.org\/abs\/2210.05666"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., Lu, J.: Point-BERT: pre-training 3D point cloud transformers with masked point modeling (2022). https:\/\/arxiv.org\/abs\/2111.14819","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P., Koltun, V.: Point transformer (2021). https:\/\/arxiv.org\/abs\/2012.09164","DOI":"10.1109\/ICCV48922.2021.01595"}],"container-title":["Lecture Notes in Computer Science","Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88977-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T05:23:06Z","timestamp":1747372986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88977-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031889769","9783031889776"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88977-6_20","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":"17 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}