{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T10:39:35Z","timestamp":1769596775940,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T00:00:00Z","timestamp":1723852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T00:00:00Z","timestamp":1723852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806024"],"award-info":[{"award-number":["61806024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206277"],"award-info":[{"award-number":["62206277"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013141","name":"Jilin Provincial Key Research and Development Plan Project","doi-asserted-by":"publisher","award":["20210204070YY"],"award-info":[{"award-number":["20210204070YY"]}],"id":[{"id":"10.13039\/501100013141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wuxi University Research Start-up Fund for Introduced Talents","award":["2023r004"],"award-info":[{"award-number":["2023r004"]}]},{"name":"Wuxi University Research Start-up Fund for Introduced Talents","award":["2023r005"],"award-info":[{"award-number":["2023r005"]}]},{"name":"Wuxi University Research Start-up Fund for Introduced Talents","award":["2023r006"],"award-info":[{"award-number":["2023r006"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s40747-024-01593-y","type":"journal-article","created":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T09:02:30Z","timestamp":1723885350000},"page":"8213-8231","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TSKPD: twin structure key point detection in point cloud"],"prefix":"10.1007","volume":"10","author":[{"given":"Yangyue","family":"Feng","sequence":"first","affiliation":[]},{"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lijuan","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2907-9811","authenticated-orcid":false,"given":"Yan","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Jinfang","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,17]]},"reference":[{"key":"1593_CR1","doi-asserted-by":"crossref","unstructured":"Zhong Y (2010) Intrinsic shape signatures: a shape descriptor for 3d object recognition. pp. 689\u201396","DOI":"10.1109\/ICCVW.2009.5457637"},{"key":"1593_CR2","first-page":"1912","volume":"30","author":"Z Wu","year":"2015","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3D ShapeNets: A deep representation for volumetric shapes. Proc IEEE\/CVF Conf Comput Vis Pattern Recogn 30:1912\u20131920","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recogn"},{"key":"1593_CR3","doi-asserted-by":"crossref","unstructured":"Xiong, K, Gong S, Ye X, Tan X, Wan J, Ding E, Wang J, Bai X (2023) Cape: camera view position embedding for multi-view 3d object detection. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 21570\u201321579","DOI":"10.1109\/CVPR52729.2023.02066"},{"key":"1593_CR4","doi-asserted-by":"crossref","unstructured":"Li J, Lee GH (2019) USIP: unsupervised stable interest point detection from 3d point clouds. In: Proceedings of the IEEE international conference on computer vision. pp. 361\u201370","DOI":"10.1109\/ICCV.2019.00045"},{"key":"1593_CR5","doi-asserted-by":"crossref","unstructured":"Yew ZJ, Lee GH (2018) 3DFeatNet: weakly supervised local 3d features for point cloud registration. In: European conference on computer vision. pp. 607\u201323","DOI":"10.1007\/978-3-030-01267-0_37"},{"issue":"3","key":"1593_CR6","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1007\/s40747-021-00376-z","volume":"8","author":"S Li","year":"2022","unstructured":"Li S, Liu B, Li S, Zhu X, Yan Y, Zhang D (2022) A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment. Complex Intell Syst 8(3):1929\u20131939","journal-title":"Complex Intell Syst"},{"key":"1593_CR7","doi-asserted-by":"crossref","unstructured":"Su K, Yu D, Xu Z, Geng X, Wang C (2019) Multi-person pose estimation with enhanced channel-wise and spatial information. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 5674\u201382","DOI":"10.1109\/CVPR.2019.00582"},{"key":"1593_CR8","doi-asserted-by":"crossref","unstructured":"Li J, Wang C, Zhu H, Mao Y, Fang HS, Lu C (2019) Crowdpose: efficient crowded scenes pose estimation and a new benchmark. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. Springer. pp. 10863\u201310872","DOI":"10.1109\/CVPR.2019.01112"},{"key":"1593_CR9","unstructured":"Li J, Wang C, Zhu H, Mao Y, Fang HS, Lu C (2021) Bottom-up human pose estimation via disentangled keypoint regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 14676\u2013686"},{"key":"1593_CR10","first-page":"6278","volume":"34","author":"D Wang","year":"2021","unstructured":"Wang D, Zhang S, Hua G (2021) Robust pose estimation in crowded scenes with direct pose-level inference. Adv Neural Inf Process Syst 34:6278\u20136289","journal-title":"Adv Neural Inf Process Syst"},{"key":"1593_CR11","doi-asserted-by":"crossref","unstructured":"Zhong Y (2009) Intrinsic shape signatures: a shape descriptor for 3d object recognition. In: IEEE international conference on computer vision workshops. IEEE. pp. 689\u201396","DOI":"10.1109\/ICCVW.2009.5457637"},{"key":"1593_CR12","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/s11263-009-0296-z","volume":"89","author":"A Mian","year":"2010","unstructured":"Mian A, Bennamoun M, Owens R (2010) On the repeatability and quality of keypoints for local feature-based 3d object retrieval from cluttered scenes. Int J Comput Vis 89:348\u20133461","journal-title":"Int J Comput Vis"},{"key":"1593_CR13","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1111\/j.1467-8659.2009.01515.x","volume":"28","author":"J Sun","year":"2009","unstructured":"Sun J, Ovsjanikov M, Guibas L (2009) HKS: a concise and provably informative multi-scale signature based on heat diffusion. Comput Graph Forum 28:1383\u20131392","journal-title":"Comput Graph Forum"},{"key":"1593_CR14","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1016\/j.patrec.2007.02.009","volume":"28","author":"J Liu","year":"2007","unstructured":"Liu J, Huang S, Nowinski WL (2007) 3d free-form object recognition in range images using local surface patches. Pattern Recogn Lett 28:1252\u20131262","journal-title":"Pattern Recogn Lett"},{"issue":"10","key":"1593_CR15","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1109\/34.625113","volume":"19","author":"C Dorai","year":"1997","unstructured":"Dorai C, Jain AK (1997) Cosmos\u2014a representation scheme for 3d free-form objects. IEEE Trans Pattern Anal Mach Intell 19(10):1115\u20131130","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1593_CR16","doi-asserted-by":"crossref","unstructured":"Yew ZJ, Lee GH (2018) 3dfeat-net: weakly supervised local 3d features for point cloud registration. In: European conference on computer vision. IEEE. pp. 607\u201323","DOI":"10.1007\/978-3-030-01267-0_37"},{"key":"1593_CR17","doi-asserted-by":"crossref","unstructured":"Chen X, He K. Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition. IEEE; 2021","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"1593_CR18","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J. 3d shapenets: a deep representation for volumetric shapes. 2015;30:1912\u20131920"},{"key":"1593_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2980238","volume":"35","author":"L Yi","year":"2016","unstructured":"Yi L, Kim VG, Ceylan D, Shen IC, Yan M, Su H, Lu C, Huang Q, Sheffer A, Guibas L (2016) A scalable active framework for region annotation in 3d shape collections. ACM Trans Graph 35:1\u201312","journal-title":"ACM Trans Graph"},{"key":"1593_CR20","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s40747-020-00206-8","volume":"7","author":"C Ning","year":"2021","unstructured":"Ning C, Menglu L, Hao Y, Xueping S, Yunhong L (2021) Survey of pedestrian detection with occlusion. Complex Intell Syst 7:577\u2013587","journal-title":"Complex Intell Syst"},{"key":"1593_CR21","first-page":"1","volume":"10","author":"Y Li","year":"2023","unstructured":"Li Y, Wu L, Chen Y, Wang X, Yin G, Wang Z (2023) Motion estimation and multi-stage association for tracking-by-detection. Complex Intell Syst 10:1\u201314","journal-title":"Complex Intell Syst"},{"key":"1593_CR22","unstructured":"Devries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv:1708.04552"},{"key":"1593_CR23","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. 2012. pp. 212\u201323"},{"key":"1593_CR24","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y. Cutmix: regularization strategy to train strong classifiers with localizable features. Clova AI Research. NAVER Corp.; Clova AI Research. LINE Plus; Yonsei University 2(3), 6022\u20136031. IEEE. 2019","DOI":"10.1109\/ICCV.2019.00612"},{"key":"1593_CR25","first-page":"215","volume":"58","author":"X She","year":"2022","unstructured":"She X, Li R, Ye O (2022) Pedestrian re-identification combining random erasing and residual attention network. Comput Eng Appl 58:215\u2013221","journal-title":"Comput Eng Appl"},{"key":"1593_CR26","doi-asserted-by":"crossref","unstructured":"Wang X, Xiao T, Jiang Y, Shao S, Shen C (2018) Repulsion loss: detecting pedestrians in a crowd. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 7774\u201383","DOI":"10.1109\/CVPR.2018.00811"},{"key":"1593_CR27","doi-asserted-by":"crossref","unstructured":"Zhang S, Wen L, Bian X, Lei Z, Li SZ. Occlusion-aware rcnn: detecting pedestrians in a crowd. In: Proceedings of the European conference on computer vision (ECCV). 2018. pp. 657\u201374","DOI":"10.1007\/978-3-030-01219-9_39"},{"key":"1593_CR28","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang Z, Peng Y, Zhang Z, Sun J. CPN: cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2018. pp. 7103\u201312","DOI":"10.1109\/CVPR.2018.00742"},{"key":"1593_CR29","doi-asserted-by":"crossref","unstructured":"Ye M, Xu S, Cao T (2020) Peeking into occluded joints: a novel framework for crowd pose estimation. In: Computer vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIX 16, vol. 19. pp. 488\u2013504","DOI":"10.1007\/978-3-030-58529-7_29"},{"key":"1593_CR30","doi-asserted-by":"crossref","unstructured":"Khirodkar R, Chari V, Agrawal A, Tyagi A. Multi-instance pose networks: rethinking top-down pose estimation. 2021;3122\u201331","DOI":"10.1109\/ICCV48922.2021.00311"},{"key":"1593_CR31","unstructured":"Minemura K, Liau H, Monrroy A, Kato S. Contextual instance decoupling for robust multi-person pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE; 2022. pp. 11060\u201368"},{"key":"1593_CR32","doi-asserted-by":"crossref","unstructured":"Steder B, Rusu RB, Konolige K, Burgard W. Point feature extraction on 3d range scans taking into account object boundaries. In: IEEE international conference on robotics and automation. 2011. pp. 2601\u201308","DOI":"10.1109\/ICRA.2011.5980187"},{"key":"1593_CR33","doi-asserted-by":"crossref","unstructured":"Unnikrishnan R, Hebert M. LBSS: multi-scale interest regions from unorganized point clouds. In: IEEE computer society conference on computer vision and pattern recognition workshops. 2008. pp. 1\u20138","DOI":"10.1109\/CVPRW.2008.4563030"},{"key":"1593_CR34","doi-asserted-by":"crossref","unstructured":"Zaharescu A, Boyer E, Varanasi K, Horaud R. Surface feature detection and description with applications to mesh matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, vol. 58. 2009; p. 373\u201380","DOI":"10.1109\/CVPR.2009.5206748"},{"key":"1593_CR35","doi-asserted-by":"crossref","unstructured":"Umberto C, Marco C, Simone F, Vittorio M. Sparse points matching by combining 3d mesh saliency with statistical descriptors. In: Computer graphics forum, vol. 27. IEEE; 2008. pp. 643\u2013652","DOI":"10.1111\/j.1467-8659.2008.01162.x"},{"issue":"9","key":"1593_CR36","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen T (1990) The self-organizing map. IEEE Proc Icnn 78(9):1464\u20131480","journal-title":"IEEE Proc Icnn"},{"key":"1593_CR37","doi-asserted-by":"crossref","unstructured":"Li J, Chen BM, Lee GH. So-net: self-organizing network for point cloud analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp 9397\u2013406","DOI":"10.1109\/CVPR.2018.00979"},{"key":"1593_CR38","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) PointNet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652\u201360"},{"key":"1593_CR39","doi-asserted-by":"crossref","unstructured":"You Y, Liu W, Ze Y, Li Y-L, Wang W, Lu C (2022) UKPGAN: a general self-supervised keypoint detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 17042\u201351","DOI":"10.1109\/CVPR52688.2022.01653"},{"key":"1593_CR40","doi-asserted-by":"crossref","unstructured":"Zohaib M, Del\u00a0Bue A. SC3K: self-supervised and coherent 3d keypoints estimation from rotated, noisy, and decimated point cloud data. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2023. pp. 22509\u201319","DOI":"10.1109\/ICCV51070.2023.02057"},{"key":"1593_CR41","doi-asserted-by":"crossref","unstructured":"Fernandez-Labrador C, Chhatkuli A, Paudel DP, Guerrero JJ, Demonceaux C, Gool LV. Unsupervised learning of category-specific symmetric 3d keypoints from point sets. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXV 16. Springer; 2020. pp. 546\u201363","DOI":"10.1007\/978-3-030-58595-2_33"},{"key":"1593_CR42","doi-asserted-by":"crossref","unstructured":"Shi R, Xue Z, You Y, Lu C. Skeleton merger: an unsupervised aligned keypoint detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021. pp. 43\u201352","DOI":"10.1109\/CVPR46437.2021.00011"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01593-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01593-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01593-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T22:19:35Z","timestamp":1729117175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01593-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,17]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1593"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01593-y","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,17]]},"assertion":[{"value":"15 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"The authors declare that they have no Conflict of interest to report regarding the present study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}]}}