{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:38:41Z","timestamp":1772721521856,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1605254"],"award-info":[{"award-number":["U1605254"]}],"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":["61872306"],"award-info":[{"award-number":["61872306"]}],"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":["61701191"],"award-info":[{"award-number":["61701191"]}],"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":["41871380"],"award-info":[{"award-number":["41871380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2018J05108"],"award-info":[{"award-number":["2018J05108"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xia-men Science and Technology Bureau","award":["3502Z20193017"],"award-info":[{"award-number":["3502Z20193017"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M690094"],"award-info":[{"award-number":["2021M690094"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["20720210074"],"award-info":[{"award-number":["20720210074"]}],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971363"],"award-info":[{"award-number":["61971363"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10489-022-03372-z","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T09:03:12Z","timestamp":1646298192000},"page":"14178-14193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["2D3D-MVPNet: Learning cross-domain feature descriptors for 2D-3D matching based on multi-view projections of point clouds"],"prefix":"10.1007","volume":"52","author":[{"given":"Baiqi","family":"Lai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-1139","authenticated-orcid":false,"given":"Weiquan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoliang","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Yangbin","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Xuesheng","family":"Bian","sequence":"additional","affiliation":[]},{"given":"Shangbin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"issue":"9","key":"3372_CR1","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1109\/LGRS.2019.2949351","volume":"17","author":"W Liu","year":"2019","unstructured":"Liu W, Wang C, Bian X, Chen S, Yu S, Lin X, Lai S-H, Weng D, Li J (2019) Learning to match ground camera image and uav 3-d model-rendered image based on siamese network with attention mechanism. IEEE Geosci Remote Sens Lett 17(9):1608\u20131612","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"14","key":"3372_CR2","doi-asserted-by":"publisher","first-page":"4819","DOI":"10.3390\/s21144819","volume":"21","author":"Y Li","year":"2021","unstructured":"Li Y, Wang Z (2021) 3d reconstruction with single-shot structured light rgb line pattern. Sensors 21(14):4819","journal-title":"Sensors"},{"key":"3372_CR3","first-page":"1","volume":"70","author":"Y Li","year":"2020","unstructured":"Li Y, Wang Z (2020) Rgb line pattern-based stereo vision matching for single-shot 3-d measurement. IEEE Trans Instrum Meas 70:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"3372_CR4","doi-asserted-by":"publisher","first-page":"108195","DOI":"10.1016\/j.measurement.2020.108195","volume":"167","author":"YC Shuang","year":"2021","unstructured":"Shuang YC, Wang ZZ (2021) Active stereo vision three-dimensional reconstruction by rgb dot pattern projection and ray intersection. Meas 167:108195","journal-title":"Meas"},{"key":"3372_CR5","doi-asserted-by":"publisher","first-page":"107405","DOI":"10.1016\/j.asoc.2021.107405","volume":"108","author":"Wu Yi","year":"2021","unstructured":"Yi W u, Jiang X, Fang Z, Gao Y, Fujita H (2021) Multi-modal 3d object detection by 2d-guided precision anchor proposal and multi-layer fusion. Appl Soft Comput 108:107405","journal-title":"Appl Soft Comput"},{"key":"3372_CR6","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/JSTARS.2020.3035359","volume":"14","author":"W Liu","year":"2020","unstructured":"Liu W, Lai B, Wang C, Cai G, Yanfei S u, Bian X, Li Y, Chen S, Li J (2020) Ground camera image and large-scale 3-d image-based point cloud registration based on learning domain invariant feature descriptors. IEEE J Sel Top Appl Earth Obs Remote Sens 14:997\u20131009","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"3372_CR7","doi-asserted-by":"crossref","unstructured":"Li Y, Snavely N, Huttenlocher D, Fua P (2012) Worldwide pose estimation using 3d point clouds. In: European conference on computer vision (ECCV), Springer, pp 15\u201329","DOI":"10.1007\/978-3-642-33718-5_2"},{"issue":"2","key":"3372_CR8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.robot.2009.09.010","volume":"58","author":"C Valgren","year":"2010","unstructured":"Valgren C, Lilienthal AJ (2010) Sift, surf & seasons: Appearance-based long-term localization in outdoor environments. Robot Auton Syst 58(2):149\u2013156","journal-title":"Robot Auton Syst"},{"issue":"9","key":"3372_CR9","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1109\/TPAMI.2016.2611662","volume":"39","author":"T Sattler","year":"2016","unstructured":"Sattler T, Leibe B, Kobbelt L (2016) Efficient & effective prioritized matching for large-scale image-based localization. IEEE Trans Pattern Anal Mach Intell 39(9):1744\u20131756","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3372_CR10","doi-asserted-by":"crossref","unstructured":"Feng M, Hu S, Ang MH, Lee GH (2019) 2d3d-matchnet: Learning to match keypoints across 2d image and 3d point cloud. In: 2019 International conference on robotics and automation (ICRA), IEEE, pp 4790\u20134796","DOI":"10.1109\/ICRA.2019.8794415"},{"key":"3372_CR11","doi-asserted-by":"crossref","unstructured":"Liu W, Lai B, Wang C, Bian X, Yang W, Xia Y, Lin X, Lai S-H, Weng D, Li J (2020) Learning to match 2d images and 3d lidar point clouds for outdoor augmented reality. In: 2020 IEEE Conference on virtual reality and 3d user interfaces abstracts and workshops (VRW), IEEE, pp 654\u2013655","DOI":"10.1109\/VRW50115.2020.00178"},{"key":"3372_CR12","doi-asserted-by":"crossref","unstructured":"Pham Q-H, Uy MA, Hua B-S, Nguyen DT, Roig G, Yeung S-K (2020) Lcd: Learned cross-domain descriptors for 2d-3d matching. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), vol 34, pp 11856\u201311864","DOI":"10.1609\/aaai.v34i07.6859"},{"key":"3372_CR13","unstructured":"Qi CR, Hao S u, 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 (CVPR), pp 652\u2013660"},{"key":"3372_CR14","doi-asserted-by":"crossref","unstructured":"Xing X, Cai Y, Lu T, Cai S, Yang Y, Wen D (2018) 3dtnet: Learning local features using 2d and 3d cues. In: 2018 International conference on 3d vision (3DV), IEEE, pp 435\u2013443","DOI":"10.1109\/3DV.2018.00057"},{"key":"3372_CR15","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 (CVPR), pp 1802\u20131811","DOI":"10.1109\/CVPR.2017.29"},{"key":"3372_CR16","unstructured":"Han X, Leung T, Jia Y, Sukthankar R, Berg AC (2015) Matchnet: Unifying feature and metric learning for patch-based matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3279\u20133286"},{"key":"3372_CR17","doi-asserted-by":"crossref","unstructured":"Simo-Serra E, Trulls E, Ferraz L, Kokkinos I, Fua P, Moreno-Noguer F (2015) Discriminative learning of deep convolutional feature point descriptors. In: Proceedings of the IEEE international conference on computer vision (ICCV) pp 118\u2013126","DOI":"10.1109\/ICCV.2015.22"},{"key":"3372_CR18","doi-asserted-by":"crossref","unstructured":"Yang Tsun-Yi, Hsu Jo-Han, Lin Yen-Yu, Chuang Yung-Yu (2017) Deepcd: Learning deep complementary descriptors for patch representations. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3314\u20133322","DOI":"10.1109\/ICCV.2017.359"},{"key":"3372_CR19","doi-asserted-by":"crossref","unstructured":"Tian Y, Fan B, Fuchao W u (2017) L2-net: Deep learning of discriminative patch descriptor in euclidean space. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 661\u2013669","DOI":"10.1109\/CVPR.2017.649"},{"key":"3372_CR20","doi-asserted-by":"crossref","unstructured":"Liu W, Shen X, Wang C, Zhang Z, Wen C, Li J (2018) H-net: neural network for cross-domain image patch matching. In: International joint conference on artificial intelligence (IJCAI), pp 856\u2013863","DOI":"10.24963\/ijcai.2018\/119"},{"issue":"4","key":"3372_CR21","doi-asserted-by":"publisher","first-page":"430","DOI":"10.3390\/rs11040430","volume":"11","author":"Y Dong","year":"2019","unstructured":"Dong Y, Jiao W, Long T, Liu L, He G, Gong C, Guo Y (2019) Local deep descriptor for remote sensing image feature matching. Remote Sens 11(4):430","journal-title":"Remote Sens"},{"issue":"19","key":"3372_CR22","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.3390\/rs11192243","volume":"11","author":"W Liu","year":"2019","unstructured":"Liu W, Wang C, Bian X, Chen S, Li W, Lin X, Li Y, Weng D, Lai S-H, Li J (2019) Ae-gan-net: Learning invariant feature descriptor to match ground camera images and a large-scale 3d image-based point cloud for outdoor augmented reality. Remote Sens 11(19):2243","journal-title":"Remote Sens"},{"key":"3372_CR23","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"3372_CR24","doi-asserted-by":"crossref","unstructured":"He K, Yan L u, Sclaroff S (2018) Local descriptors optimized for average precision. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 596\u2013605","DOI":"10.1109\/CVPR.2018.00069"},{"key":"3372_CR25","doi-asserted-by":"crossref","unstructured":"Keller M, Chen Z, Maffra F, Schmuck P, Chli M (2018) Learning deep descriptors with scale-aware triplet networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2762\u20132770","DOI":"10.1109\/CVPR.2018.00292"},{"key":"3372_CR26","doi-asserted-by":"crossref","unstructured":"DeTone D, Malisiewicz T, Rabinovich A (2018) Superpoint: Self-supervised interest point detection and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 224\u2013236","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"3372_CR27","unstructured":"Revaud J, Weinzaepfel P, Souza C\u00e9sar D, Pion N, Csurka G, Cabon Y, Humenberger M (2019) R2d2: Repeatable and reliable detector and descriptor. CoRR, arXiv:abs\/1906.06195"},{"key":"3372_CR28","doi-asserted-by":"crossref","unstructured":"Dusmanu M, Rocco I, Pajdla T, Pollefeys M, Sivic J, Torii A, Sattler T (2019) D2-net: A trainable cnn for joint description and detection of local features. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8092\u20138101","DOI":"10.1109\/CVPR.2019.00828"},{"key":"3372_CR29","doi-asserted-by":"crossref","unstructured":"Luo Z, Zhou L, Bai X, Chen H, Zhang J, Yao Y, Li S, Fang T, Quan L (2020) Aslfeat: Learning local features of accurate shape and localization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6589\u20136598","DOI":"10.1109\/CVPR42600.2020.00662"},{"key":"3372_CR30","first-page":"5099","volume":"30","author":"CR Qi","year":"2017","unstructured":"Qi CR, Li Y i, Hao S u, Guibas LJ (2017) Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv Neural Inform Process Syst 30:5099\u20135108","journal-title":"Adv Neural Inform Process Syst"},{"key":"3372_CR31","doi-asserted-by":"crossref","unstructured":"Jiang M, Wu Y, Zhao T, Zhao Z, Lu C (2018) Pointsift: A sift-like network module for 3d point cloud semantic segmentation. arXiv:1807.00652","DOI":"10.1109\/IGARSS.2019.8900102"},{"key":"3372_CR32","first-page":"820","volume":"31","author":"Y Li","year":"2018","unstructured":"Li Y, Rui B u, Sun M, Wei W u, Di X, Chen B (2018) Pointcnn: Convolution on x-transformed points. Adv Neural Inform Process Syst 31:820\u2013830","journal-title":"Adv Neural Inform Process Syst"},{"key":"3372_CR33","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 (CVPR), pp 5545\u20135554","DOI":"10.1109\/CVPR.2019.00569"},{"key":"3372_CR34","doi-asserted-by":"crossref","unstructured":"Deng H, Birdal T, Ilic S (2018) Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors. In: Proceedings of the European conference on computer vision (ECCV), pp 602\u2013618","DOI":"10.1007\/978-3-030-01228-1_37"},{"key":"3372_CR35","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 (ICCV), pp 8958\u20138966","DOI":"10.1109\/ICCV.2019.00905"},{"key":"3372_CR36","doi-asserted-by":"crossref","unstructured":"Yew ZJ, Lee GH (2018) 3dfeat-net: Weakly supervised local 3d features for point cloud registration. In: Proceedings of the European conference on computer vision (ECCV), pp 607\u2013623","DOI":"10.1007\/978-3-030-01267-0_37"},{"key":"3372_CR37","doi-asserted-by":"crossref","unstructured":"Bai X, Luo Z, Zhou L, Fu H, Quan L, Tai C-L (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 (CVPR), pp 6359\u20136367","DOI":"10.1109\/CVPR42600.2020.00639"},{"key":"3372_CR38","doi-asserted-by":"crossref","unstructured":"Su H, Maji S, Kalogerakis E, Learned-Miller E (2015) Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 945\u2013953","DOI":"10.1109\/ICCV.2015.114"},{"key":"3372_CR39","doi-asserted-by":"crossref","unstructured":"Feng Y, Zhang Z, Zhao X, Ji R, Gao Y (2018) Gvcnn: Group-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 264\u2013272","DOI":"10.1109\/CVPR.2018.00035"},{"key":"3372_CR40","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1912\u20131920"},{"key":"3372_CR41","doi-asserted-by":"crossref","unstructured":"Riegler G, Ulusoy AO, Geiger A (2017) Octnet: Learning deep 3d representations at high resolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3577\u20133586","DOI":"10.1109\/CVPR.2017.701"},{"key":"3372_CR42","doi-asserted-by":"crossref","unstructured":"Landrieu L, Simonovsky M (2018) Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4558\u20134567","DOI":"10.1109\/CVPR.2018.00479"},{"key":"3372_CR43","doi-asserted-by":"crossref","unstructured":"Shi S, Guo C, Li J, Wang Z, Shi J, Wang X, Li H (2020) Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In: In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 10529\u201310538","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"3372_CR44","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.isprsjprs.2021.04.011","volume":"176","author":"A Xiao","year":"2021","unstructured":"Xiao A, Yang X, Lu S, Guan D, Huang J (2021) Fps-net: a convolutional fusion network for large-scale lidar point cloud segmentation. ISPRS J Photogramm Remote Sens 176:237\u2013249","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"3372_CR45","doi-asserted-by":"crossref","unstructured":"Zhong Y u (2009) Intrinsic shape signatures: A shape descriptor for 3d object recognition. In: IEEE International conference on computer vision workshops, ICCV workshops, IEEE, pp 689\u2013696","DOI":"10.1109\/ICCVW.2009.5457637"},{"key":"3372_CR46","doi-asserted-by":"crossref","unstructured":"Huai Y u, Zhen W, Yang W, Ji Z, Scherer S (2020) Monocular camera localization in prior lidar maps with 2d-3d line correspondences. In: 2020 IEEE\/RSJ International conference on intelligent robots and systems (IROS), IEEE, pp 4588\u20134594","DOI":"10.1109\/IROS45743.2020.9341690"},{"key":"3372_CR47","doi-asserted-by":"crossref","unstructured":"Li J, Lee GH (2021) Deepi2p: Image-to-point cloud registration via deep classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 15960\u201315969","DOI":"10.1109\/CVPR46437.2021.01570"},{"key":"3372_CR48","doi-asserted-by":"crossref","unstructured":"Cattaneo D, Vaghi M, Fontana S, Ballardini AL, Sorrenti DG (2020) Global visual localization in lidar-maps through shared 2d-3d embedding space. In: IEEE international conference on robotics and automation (ICRA), IEEE, pp 4365\u20134371","DOI":"10.1109\/ICRA40945.2020.9196859"},{"key":"3372_CR49","unstructured":"Mishchuk A, Mishkin D, Radenovic F, Matas J (2017) Working hard to know your neighbor\u2019s margins: Local descriptor learning loss. In: Advances in neural information processing systems, pp 4826\u20134837"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03372-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03372-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03372-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T09:28:15Z","timestamp":1664616495000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03372-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,3]]},"references-count":49,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["3372"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03372-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,3]]},"assertion":[{"value":"9 February 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}