{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:44:25Z","timestamp":1765190665731,"version":"3.46.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-08111-y","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:39:18Z","timestamp":1765190358000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HEFPA: hyperbolic embedding and fast position-aware network for point cloud registration"],"prefix":"10.1007","volume":"81","author":[{"given":"Yang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuting","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"8111_CR1","doi-asserted-by":"crossref","unstructured":"Bai X, Luo Z, Zhou L, Fu H, Quan L, Tai CL (2020) 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","DOI":"10.1109\/CVPR42600.2020.00639"},{"key":"8111_CR2","doi-asserted-by":"crossref","unstructured":"Choy C, Park J, Koltun V (2019) Fully convolutional geometric features. In: Proceedings of the IEEE\/CVF international Conference on Computer Vision, pp. 8958\u20138966","DOI":"10.1109\/ICCV.2019.00905"},{"key":"8111_CR3","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, 2021, pp. 4267\u20134276","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"8111_CR4","doi-asserted-by":"crossref","unstructured":"Qin Z, Yu H, Wang C, Guo Y, Peng Y, Xu K (2022) 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","DOI":"10.1109\/CVPR52688.2022.01086"},{"key":"8111_CR5","unstructured":"Yu H, Li F, Saleh M, Busam B, Ilic S (2021) Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration, Adv Neural Inf Processing Syst, vol.\u00a034, pp. 23872\u201323884"},{"key":"8111_CR6","doi-asserted-by":"crossref","unstructured":"Thomas H, Qi CR, Deschaud JE, Marcotegui B, Goulette F, Guibas LJ (2019) Kpconv: Flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6411\u20136420","DOI":"10.1109\/ICCV.2019.00651"},{"key":"8111_CR7","doi-asserted-by":"crossref","unstructured":"Yu H, Qin Z, Hou J, Saleh M, Li D, Busam B, Ilic S (2023) Rotation-invariant transformer for point cloud matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5384\u20135393","DOI":"10.1109\/CVPR52729.2023.00521"},{"key":"8111_CR8","doi-asserted-by":"crossref","unstructured":"Peng W, Varanka T, Mostafa A, Shi H, Zhao G (2021) Hyperbolic deep neural networks: a survey. IEEE Trans Pattern Anal Mach Intell, 44(12): 10023\u201310044","DOI":"10.1109\/TPAMI.2021.3136921"},{"key":"8111_CR9","unstructured":"Yang M, Zhou M, Ying R, Chen Y, King I (2023) Hyperbolic representation learning: revisiting and advancing. In: International Conference on Machine Learning. PMLR, pp. 39639\u201339659"},{"key":"8111_CR10","unstructured":"Ganea O, \u00e9cigneul GB, Hofmann T (2018) Hyperbolic neural networks. Advances in Neural Information Processing Systems, vol.\u00a031"},{"key":"8111_CR11","first-page":"33741","volume":"35","author":"A Montanaro","year":"2022","unstructured":"Montanaro A, Valsesia D, Magli E (2022) Rethinking the compositionality of point clouds through regularization in the hyperbolic space. Adv Neural Inf Process Syst 35:33741\u201333753","journal-title":"Adv Neural Inf Process Syst"},{"key":"8111_CR12","doi-asserted-by":"crossref","unstructured":"Wang Y, Solomon JM (2019) Deep closest point: learning representations for point cloud registration. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3523\u20133532","DOI":"10.1109\/ICCV.2019.00362"},{"key":"8111_CR13","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in Neural Information Processing Systems, vol.\u00a030"},{"key":"8111_CR14","doi-asserted-by":"crossref","unstructured":"Yang J, Zhang Q, Ni B, Li L, Liu J, Zhou M, Tian Q, (2019) Modeling point clouds with self-attention and gumbel subset sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3323\u20133332","DOI":"10.1109\/CVPR.2019.00344"},{"key":"8111_CR15","doi-asserted-by":"crossref","unstructured":"Zhao H, Jiang L, Jia J, Torr PH, Koltun V (2021) Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16259\u201316268","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"8111_CR16","doi-asserted-by":"crossref","unstructured":"Yao R, Du S, Cui W, Tang C, Yang C (2024) Pare-net: position-aware rotation-equivariant networks for robust point cloud registration. In: European Conference on Computer Vision. Springer, pp. 287\u2013303","DOI":"10.1007\/978-3-031-72904-1_17"},{"key":"8111_CR17","doi-asserted-by":"crossref","unstructured":"Ao S, Hu Q, Wang H, Xu K, Guo Y (2023) 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","DOI":"10.1109\/CVPR52729.2023.00127"},{"key":"8111_CR18","doi-asserted-by":"crossref","unstructured":"Wang H, Liu Y, Dong Z, Wang W (2022) You only hypothesize once: point cloud registration with rotation-equivariant descriptors. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1630\u20131641","DOI":"10.1145\/3503161.3548023"},{"issue":"8","key":"8111_CR19","doi-asserted-by":"publisher","first-page":"10376","DOI":"10.1109\/TPAMI.2023.3244951","volume":"45","author":"H Wang","year":"2023","unstructured":"Wang H, Liu Y, Hu Q, Wang B, Chen J, Dong Z, Guo Y, Wang W, Yang B (2023) RoReg: pairwise point cloud registration with oriented descriptors and local rotations. IEEE Trans Pattern Anal Mach Intell 45(8):10376\u201310393","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8111_CR20","doi-asserted-by":"crossref","unstructured":"Yu J, Ren L, Zhang Y, Zhou W, Lin L, Dai G (2023) Peal: prior-embedded explicit attention learning for low-overlap point cloud registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17702\u201317711","DOI":"10.1109\/CVPR52729.2023.01698"},{"key":"8111_CR21","doi-asserted-by":"crossref","unstructured":"Deng C, Litany O, Duan Y, Poulenard A, Tagliasacchi A, Guibas LJ (2021) Vector neurons: A general framework for so (3)-equivariant networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12200\u201312209","DOI":"10.1109\/ICCV48922.2021.01198"},{"key":"8111_CR22","doi-asserted-by":"crossref","unstructured":"Ao S, Hu Q, Yang B, Markham A, Guo Y ( 2021) Spinnet: Learning a general surface descriptor for 3d point cloud registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11753\u201311762","DOI":"10.1109\/CVPR46437.2021.01158"},{"key":"8111_CR23","doi-asserted-by":"crossref","unstructured":"Sarkar R (2011) Low distortion delaunay embedding of trees in hyperbolic plane. In International Symposium on Graph Drawing. Springer, pp. 355\u2013366","DOI":"10.1007\/978-3-642-25878-7_34"},{"key":"8111_CR24","unstructured":"Chami I, Ying Z, R\u00e9 C, Leskovec J (2019) Hyperbolic graph convolutional neural networks. In: Advances in Neural Information Processing Systems, vol.\u00a032"},{"key":"8111_CR25","doi-asserted-by":"crossref","unstructured":"Ermolov A, Mirvakhabova L, Khrulkov V, Sebe N, Oseledets I (2022) Hyperbolic vision transformers: combining improvements in metric learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7409\u20137419","DOI":"10.1109\/CVPR52688.2022.00726"},{"key":"8111_CR26","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et\u00a0al., (2020) An image is worth 16x16 words: transformers for image recognition at scale, arXiv preprint arXiv:2010.11929,"},{"key":"8111_CR27","doi-asserted-by":"crossref","unstructured":"Wang S, Kang Q, She R, Wang W, Zhao K, Song Y, Tay WP (2023) Hypliloc: Towards effective lidar pose regression with hyperbolic fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5176\u20135185","DOI":"10.1109\/CVPR52729.2023.00501"},{"key":"8111_CR28","doi-asserted-by":"crossref","unstructured":"Deng H, Birdal T, Ilic S, (2018) PPFNet: Global context aware local features for robust 3d point matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 195\u2013205","DOI":"10.1109\/CVPR.2018.00028"},{"key":"8111_CR29","doi-asserted-by":"crossref","unstructured":"Gojcic Z, Zhou C, Wegner JD, Wieser A, (2019) The perfect match: 3D point cloud matching with smoothed densities. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5545\u20135554","DOI":"10.1109\/CVPR.2019.00569"},{"key":"8111_CR30","first-page":"1","volume":"60","author":"J Xu","year":"2022","unstructured":"Xu J, Huang Y, Wan Z, Wei J (2022) Glorn: Strong generalization fully convolutional network for low-overlap point cloud registration. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8111_CR31","doi-asserted-by":"crossref","unstructured":"Zhang X, Yang J, Zhang S, Zhang Y (2023) 3D registration with maximal cliques. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17745\u201317754","DOI":"10.1109\/CVPR52729.2023.01702"},{"key":"8111_CR32","first-page":"1","volume":"60","author":"J Yang","year":"2022","unstructured":"Yang J, Chen J, Quan S, Wang W, Zhang Y (2022) Correspondence selection with loose-tight geometric voting for 3-d point cloud registration. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8111_CR33","first-page":"1","volume":"60","author":"J Yang","year":"2021","unstructured":"Yang J, Huang Z, Quan S, Qi Z, Zhang Y (2021) Sac-cot: sample consensus by sampling compatibility triangles in graphs for 3-d point cloud registration. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8111_CR34","doi-asserted-by":"crossref","unstructured":"Yew ZJ, Lee GH, (2022) Regtr: End-to-end point cloud correspondences with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6677\u20136686","DOI":"10.1109\/CVPR52688.2022.00656"},{"key":"8111_CR35","first-page":"6982","volume":"35","author":"F Yang","year":"2022","unstructured":"Yang F, Guo L, Chen Z, Tao W (2022) One-inlier is first: towards efficient position encoding for point cloud registration. Adv Neural Inf Process Syst 35:6982\u20136995","journal-title":"Adv Neural Inf Process Syst"},{"key":"8111_CR36","doi-asserted-by":"crossref","unstructured":"Xu M, Ding R, Zhao H, Qi X, (2021) PAConv: position adaptive convolution with dynamic kernel assembling on point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3173\u20133182","DOI":"10.1109\/CVPR46437.2021.00319"},{"key":"8111_CR37","doi-asserted-by":"crossref","unstructured":"Ermolov A, Mirvakhabova L, Khrulkov V, Sebe N, Oseledets I (2022) Hyperbolic vision transformers: combining improvements in metric learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1600\u20131610. [Online]. Available: https:\/\/arxiv.org\/abs\/2203.10833","DOI":"10.1109\/CVPR52688.2022.00726"},{"key":"8111_CR38","doi-asserted-by":"crossref","unstructured":"Guo Y, Wang X, Chen Y, Yu SX (2022) Clipped hyperbolic classifiers are super-hyperbolic classifiers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11\u201320","DOI":"10.1109\/CVPR52688.2022.00011"},{"key":"8111_CR39","unstructured":"Shimizu R, Mukuta Y, Harada T (2020) Hyperbolic neural networks++, arXiv preprint arXiv:2006.08210,"},{"key":"8111_CR40","unstructured":"Mehta S, Rastegari M (2022) Separable self-attention for mobile vision transformers, arXiv preprint arXiv:2206.02680,"},{"key":"8111_CR41","doi-asserted-by":"crossref","unstructured":"Sun J, Shen Z, Wang Y, Bao H, Zhou X (2021) LoFTR: detector-free local feature matching with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8922\u20138931","DOI":"10.1109\/CVPR46437.2021.00881"},{"issue":"7","key":"8111_CR42","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ad3c60","volume":"35","author":"P Wei","year":"2024","unstructured":"Wei P, Yan G, Li Y, Fang K, Cai X, Liu W, Yang J (2024) Automatic multi-lidar calibration and refinement method. Meas Sci Technol 35(7):075203","journal-title":"Meas Sci Technol"},{"key":"8111_CR43","doi-asserted-by":"crossref","unstructured":"Lindenberger P, Sarlin PE, Pollefeys M (2023) Lightglue: local feature matching at light speed. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17\u00a0627\u201317\u00a0638","DOI":"10.1109\/ICCV51070.2023.01616"},{"key":"8111_CR44","doi-asserted-by":"crossref","unstructured":"Zeng A, Song S, Nie\u00dfner M, Fisher M, Xiao J, Funkhouser T (2017) 3Dmatch: Learning local geometric descriptors from RGB-D reconstructions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1802\u20131811","DOI":"10.1109\/CVPR.2017.29"},{"key":"8111_CR45","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"8111_CR46","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et\u00a0al. (2019) Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol.\u00a032"},{"key":"8111_CR47","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980,"},{"key":"8111_CR48","unstructured":"Feng Q, Atanasov N (2021) Fully convolutional geometric features for category-level object alignment, CoRR, vol. abs\/2103.04494, [Online]. Available: https:\/\/arxiv.org\/abs\/2103.04494"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08111-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08111-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08111-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:39:30Z","timestamp":1765190370000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08111-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":48,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["8111"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08111-y","relation":{},"ISSN":["1573-0484"],"issn-type":[{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"assertion":[{"value":"22 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1635"}}