{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T16:49:01Z","timestamp":1761929341493,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819537280","type":"print"},{"value":"9789819537297","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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\/978-981-95-3729-7_34","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T16:43:53Z","timestamp":1761929033000},"page":"410-421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometric Self-Attenuating Transformer for\u00a0Multi-instance Registration"],"prefix":"10.1007","author":[{"given":"Jianwei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoyu","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji\u2019ang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8218-3079","authenticated-orcid":false,"given":"Liang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanyu","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Ao, S., Hu, Q., Wang, H., Xu, K., Guo, Y.: BUFFER: balancing accuracy, efficiency, and generalizability in point cloud registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1255\u20131264 (2023)","DOI":"10.1109\/CVPR52729.2023.00127"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Avetisyan, A., Dahnert, M., Dai, A., Savva, M., Chang, A.X., Nie\u00dfner, M.: Scan2cad: learning cad model alignment in RGB-D scans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2614\u20132623 (2019)","DOI":"10.1109\/CVPR.2019.00272"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Bai, X., Luo, Z., Zhou, L., Fu, H., Quan, L., Tai, C.L.: D3Feat: joint learning of dense detection and description of 3D local features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6359\u20136367 (2020)","DOI":"10.1109\/CVPR42600.2020.00639"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Barath, D., Matas, J.: Progressive-X: efficient, anytime, multi-model fitting algorithm. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3780\u20133788 (2019)","DOI":"10.1109\/ICCV.2019.00388"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Barath, D., Rozumnyi, D., Eichhardt, I., Hajder, L., Matas, J.: Finding geometric models by clustering in the consensus space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5414\u20135424 (2023)","DOI":"10.1109\/CVPR52729.2023.00524"},{"key":"34_CR6","unstructured":"Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol.\u00a01611, pp. 586\u2013606. SPIE (1992)"},{"key":"34_CR7","unstructured":"Chang, A.X., et\u00a0al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Chen, G., Wang, M., Yang, Y., Yuan, L., Yue, Y.: Fast and robust point cloud registration with tree-based transformer. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 773\u2013780. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10610004"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828\u20135839 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 998\u20131005. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5540108"},{"key":"34_CR11","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"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Kluger, F., Brachmann, E., Ackermann, H., Rother, C., Yang, M.Y., Rosenhahn, B.: CONSAC: robust multi-model fitting by conditional sample consensus. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4634\u20134643 (2020)","DOI":"10.1109\/CVPR42600.2020.00469"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Magri, L., Fusiello, A.: T-linkage: a continuous relaxation of J-linkage for multi-model fitting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3954\u20133961 (2014)","DOI":"10.1109\/CVPR.2014.505"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Magri, L., Fusiello, A.: Multiple model fitting as a set coverage problem. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3318\u20133326 (2016)","DOI":"10.1109\/CVPR.2016.361"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, H., Wang, C., Guo, Y., Peng, Y., Xu, K.: Geometric transformer for fast and robust point cloud registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11143\u201311152 (2022)","DOI":"10.1109\/CVPR52688.2022.01086"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212\u20133217. IEEE (2009)","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"34_CR18","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":"34_CR19","doi-asserted-by":"crossref","unstructured":"Tang, W., Zou, D.: Multi-instance point cloud registration by efficient correspondence clustering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6667\u20136676 (2022)","DOI":"10.1109\/CVPR52688.2022.00655"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Yang, J., Cao, X., Zhang, X., Cheng, Y., Qi, Z., Quan, S.: Instance by instance: an iterative framework for multi-instance 3D registration. IEEE\/CAA J. Autom. Sinica (2025)","DOI":"10.1109\/JAS.2024.125058"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Yang, J., Gao, Y., Li, D., Waslander, S.L.: ROBI: a multi-view dataset for reflective objects in robotic bin-picking. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9788\u20139795. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9635871"},{"key":"34_CR23","first-page":"23872","volume":"34","author":"H Yu","year":"2021","unstructured":"Yu, H., Li, F., Saleh, M., Busam, B., Ilic, S.: CoFiNet: reliable coarse-to-fine correspondences for robust point cloud registration. Adv. Neural. Inf. Process. Syst. 34, 23872\u201323884 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Yu, H., Qin, Z., Hou, J., Saleh, M., Li, D., Busam, B., Ilic, S.: Rotation-invariant transformer for point cloud matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5384\u20135393 (2023)","DOI":"10.1109\/CVPR52729.2023.00521"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Yu, Z., Qin, Z., Zheng, L., Xu, K.: Learning instance-aware correspondences for robust multi-instance point cloud registration in cluttered scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19605\u201319614 (2024)","DOI":"10.1109\/CVPR52733.2024.01854"},{"key":"34_CR26","unstructured":"Yu, Z., Zheng, Q., Zhu, C., Xu, K.: Efficient and accurate multi-instance point cloud registration with iterative main cluster detection. In: Eurographics (Short Papers) (2024)"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Yuan, M., Li, Z., Jin, Q., Chen, X., Wang, M.: PointCLM: a contrastive learning-based framework for multi-instance point cloud registration. In: European Conference on Computer Vision, pp. 595\u2013611. Springer (2022)","DOI":"10.1007\/978-3-031-20077-9_35"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3729-7_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T16:43:59Z","timestamp":1761929039000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3729-7_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819537280","9789819537297"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3729-7_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","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":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 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":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}