{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:26:30Z","timestamp":1769315190032,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"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":["62006215"],"award-info":[{"award-number":["62006215"]}],"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":["62032022"],"award-info":[{"award-number":["62032022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s00521-021-06231-z","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T04:13:15Z","timestamp":1624680795000},"page":"16315-16328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel 3D shape classification algorithm: point-to-vector capsule network"],"prefix":"10.1007","volume":"33","author":[{"given":"Hailiang","family":"Ye","sequence":"first","affiliation":[]},{"given":"Zijin","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-5694","authenticated-orcid":false,"given":"Feilong","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,26]]},"reference":[{"key":"6231_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3005434","author":"Y Guo","year":"2020","unstructured":"Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2020) Deep learning for 3D point clouds: a survey. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2020.3005434","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"6231_CR2","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s00521-017-2964-1","volume":"29","author":"B Lin","year":"2018","unstructured":"Lin B, Wang F, Zhao F, Sun Y (2018) Scale invariant point feature (SIPF) for 3D point clouds and 3D multi-scale object detection. Neural Comput Appl 29(5):1209\u20131224","journal-title":"Neural Comput Appl"},{"issue":"1","key":"6231_CR3","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/MSP.2020.2984780","volume":"38","author":"S Chen","year":"2021","unstructured":"Chen S, Liu B, Feng C, Vallespi-Gonzalez C, Wellington C (2021) 3D point cloud processing and learning for autonomous driving: impacting map creation, localization, and perception. IEEE Signal Process Mag 38(1):68\u201386","journal-title":"IEEE Signal Process Mag"},{"issue":"2","key":"6231_CR4","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1109\/LRA.2021.3056369","volume":"6","author":"A Spielberg","year":"2021","unstructured":"Spielberg A, Amini A, Chin L, Matusik W, Rus D (2021) Co-learning of task and sensor placement for soft robotics. IEEE Robot Autom Lett 6(2):1208\u20131215","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"6231_CR5","doi-asserted-by":"publisher","first-page":"2060","DOI":"10.1109\/LRA.2021.3059633","volume":"6","author":"L Wiesmann","year":"2021","unstructured":"Wiesmann L, Milioto A, Chen X, Stachniss C, Behley J (2021) Deep compression for dense point cloud maps. IEEE Robot Autom Lett 6(2):2060\u20132067","journal-title":"IEEE Robot Autom Lett"},{"key":"6231_CR6","doi-asserted-by":"crossref","unstructured":"Tu C, Takeuchi E, Carballo A, Takeda K (2019) Point cloud compression for 3D LiDAR sensor using recurrent neural network with residual blocks. In: Proceedings of international conference on robotics and automation. Montreal, Canada, pp 3274\u20133280","DOI":"10.1109\/ICRA.2019.8794264"},{"key":"6231_CR7","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1109\/TIP.2019.2955239","volume":"29","author":"MM Rahman","year":"2019","unstructured":"Rahman MM, Tan Y, Xue J, Lu K (2019) Recent advances in 3D object detection in the era of deep neural networks: a survey. IEEE Trans Image Process 29:2947\u20132962","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"6231_CR8","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s00521-016-2585-0","volume":"29","author":"S Orts-Escolano","year":"2018","unstructured":"Orts-Escolano S, Garcia-Rodriguez J, Cazorla M, Morell V, Azorin J, Saval M, Garcia-Garcia A, Villena V (2018) Bioinspired point cloud representation: 3D object tracking. Neural Comput Appl 29(9):663\u2013672","journal-title":"Neural Comput Appl"},{"issue":"11","key":"6231_CR9","doi-asserted-by":"publisher","first-page":"4042","DOI":"10.1109\/TCYB.2019.2898553","volume":"49","author":"Z Shen","year":"2019","unstructured":"Shen Z, Shang X, Zhao M, Dong X, Xiong G, Wang FY (2019) A learning-based framework for error compensation in 3D printing. IEEE Trans Cybern 49(11):4042\u20134050","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"6231_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s11069-010-9634-2","volume":"61","author":"M Jaboyedoff","year":"2012","unstructured":"Jaboyedoff M, Oppikofer T, Abell\u00e1n A, Derron MH, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Hazards 61(1):5\u201328","journal-title":"Nat Hazards"},{"key":"6231_CR11","doi-asserted-by":"crossref","unstructured":"Chen X, Ma H, Wan J, Li B, Xia T (2017) Multi-view 3D object detection network for autonomous driving. In: Proceedings of IEEE conference on computer vision and pattern recognition. Honolulu, USA, pp 1907\u20131915","DOI":"10.1109\/CVPR.2017.691"},{"key":"6231_CR12","doi-asserted-by":"crossref","unstructured":"Qi CR, Su H, Niessner M, Dai A, Yan M, Guibas LJ (2016) Volumetric and multi-view CNNs for object classification on 3D data. In: Proceedings of IEEE conference on computer vision and pattern recognition. Las Vegas, USA, pp 5648\u20135656","DOI":"10.1109\/CVPR.2016.609"},{"key":"6231_CR13","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 IEEE conference on computer vision and pattern recognition. Boston, USA, pp 1912\u20131920"},{"issue":"4","key":"6231_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459787","volume":"36","author":"PS Wang","year":"2017","unstructured":"Wang PS, Liu Y, Guo YX, Sun CY, Tong X (2017) O-CNN: Octree-based convolutional neural networks for 3D shape analysis. ACM Trans Graph 36(4):1\u201311","journal-title":"ACM Trans Graph"},{"key":"6231_CR15","doi-asserted-by":"crossref","unstructured":"Riegler G, Osman Ulusoy A, Geiger A (2017) Octnet: learning deep 3D representations at high resolutions. In: Proceedings of IEEE conference on computer vision and pattern recognition. Honolulu, USA, pp 6620\u20136629","DOI":"10.1109\/CVPR.2017.701"},{"key":"6231_CR16","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of IEEE conference on computer vision and pattern recognition. Honolulu, USA, pp 77\u201385"},{"key":"6231_CR17","unstructured":"Qi CR, Yi L, Su H, Guibas LJ (2017) PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Adv Neural Information Process Syst. Long Beach, USA, pp 5099\u20135108"},{"key":"6231_CR18","doi-asserted-by":"crossref","unstructured":"Jiang L, Zhao H, Liu S, Shen X, Fu CW, Jia J (2019) Hierarchical point-edge interaction network for point cloud semantic segmentation. In: Proceedings of IEEE international conference on computer vision. Seoul, Korea (South), pp 10432\u201310440","DOI":"10.1109\/ICCV.2019.01053"},{"key":"6231_CR19","doi-asserted-by":"crossref","unstructured":"Zhao H, Jiang L, Fu CW, Jia J (2019) PointWeb: enhancing local neighborhood features for point cloud processing. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 5560\u20135568","DOI":"10.1109\/CVPR.2019.00571"},{"key":"6231_CR20","doi-asserted-by":"crossref","unstructured":"Wu W, Qi Z, Fuxin L (2019) PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 9621\u20139630","DOI":"10.1109\/CVPR.2019.00985"},{"key":"6231_CR21","first-page":"1097","volume-title":"Adv Neural Information Process Syst","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Information Process Syst. Lake Tahoe, Nevada, pp 1097\u20131105"},{"issue":"6","key":"6231_CR22","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1007\/s00521-018-3711-y","volume":"31","author":"SV Georgakopoulos","year":"2019","unstructured":"Georgakopoulos SV, Kottari K, Delibasis K, Plagianakos VP, Maglogiannis I (2019) Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters. Neural Comput Appl 31(6):1805\u20131822","journal-title":"Neural Comput Appl"},{"key":"6231_CR23","first-page":"820","volume-title":"Adv Neural Information Process Syst","author":"Y Li","year":"2018","unstructured":"Li Y, Bu R, Sun M, Wu W, Di X, Chen B (2018) PointCNN: Convolution on $$\\cal{X}$$-transformed points. Adv Neural Information Process Syst. Montr\u00e9al, Canada, pp 820\u2013830"},{"issue":"4","key":"6231_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3197517.3201301","volume":"37","author":"M Atzmon","year":"2018","unstructured":"Atzmon M, Maron H, Lipman Y (2018) Point convolutional neural networks by extension operators. ACM Trans Graph 37(4):1\u201312","journal-title":"ACM Trans Graph"},{"key":"6231_CR25","doi-asserted-by":"crossref","unstructured":"Simonovsky M, Komodakis N (2017) Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of IEEE conference on computer vision and pattern recognition, Honolulu, USA, pp 3693\u20133702","DOI":"10.1109\/CVPR.2017.11"},{"key":"6231_CR26","doi-asserted-by":"crossref","unstructured":"Liu Y, Fan B, Xiang S, Pan C (2019) Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 8895\u20138904","DOI":"10.1109\/CVPR.2019.00910"},{"issue":"5","key":"6231_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019) Dynamic graph CNN for learning on point clouds. ACM Trans Graph 38(5):1\u201312","journal-title":"ACM Trans Graph"},{"key":"6231_CR28","doi-asserted-by":"crossref","unstructured":"Shen Y, Feng C, Yang Y, Tian D (2018) Mining point cloud local structures by kernel correlation and graph pooling. In: Proceedings of IEEE conference on computer vision and pattern recognition. Salt Lake City, USA, pp 4548\u20134557","DOI":"10.1109\/CVPR.2018.00478"},{"key":"6231_CR29","doi-asserted-by":"publisher","unstructured":"Lei H, Akhtar N, Mian A (2020) Spherical kernel for efficient graph convolution on 3D point clouds. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983410","DOI":"10.1109\/TPAMI.2020.2983410"},{"key":"6231_CR30","doi-asserted-by":"crossref","unstructured":"Poulenard A, Rakotosaona MJ, Ponty Y, Ovsjanikov M (2019) Effective rotation-invariant point CNN with spherical harmonics kernels. In: Proceedings of international conference on 3D vision. Qu\u00e9bec, Canada, pp 47\u201356","DOI":"10.1109\/3DV.2019.00015"},{"key":"6231_CR31","first-page":"3856","volume-title":"Adv Neural Information Process Syst","author":"S Sabour","year":"2017","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Information Process Syst. Long Beach, USA, pp 3856\u20133866"},{"key":"6231_CR32","unstructured":"Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with EM routing. In: Proceedings of international conference on learning representations. Vancouver, Canada"},{"key":"6231_CR33","doi-asserted-by":"crossref","unstructured":"Rajasegaran J, Jayasundara V, Jayasekara S, Jayasekara H, Seneviratne S, Rodrigo R (2019) DeepCaps: going deeper with capsule networks. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 10725\u201310733","DOI":"10.1109\/CVPR.2019.01098"},{"key":"6231_CR34","doi-asserted-by":"crossref","unstructured":"Li H, Guo X, Ouyang BD, Wang X (2018) Neural network encapsulation. In: Proceedings of European conference on computer vision. Munich, Germany, pp 252\u2013267","DOI":"10.1007\/978-3-030-01252-6_16"},{"key":"6231_CR35","doi-asserted-by":"crossref","unstructured":"Kakillioglu B, Ahmad A, Velipasalar S (2018) Object classification from 3D volumetric data with 3D capsule networks. In: Proceddings of IEEE global conference on signal and information processing. Anaheim, USA, pp 385\u2013389","DOI":"10.1109\/GlobalSIP.2018.8646333"},{"key":"6231_CR36","doi-asserted-by":"crossref","unstructured":"Cheraghian A, Petersson L (2019) 3DCapsule: extending the capsule architecture to classify 3D point clouds. In: Proceedings of IEEE winter conference on applications of computer vision. Waikoloa Village, USA, pp 1194\u20131202","DOI":"10.1109\/WACV.2019.00132"},{"key":"6231_CR37","doi-asserted-by":"crossref","unstructured":"Zhao Y, Birdal T, Deng H, Tombari F (2019) 3D point capsule networks. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 1009\u20131018","DOI":"10.1109\/CVPR.2019.00110"},{"key":"6231_CR38","doi-asserted-by":"publisher","first-page":"27393","DOI":"10.1109\/ACCESS.2020.2971950","volume":"8","author":"B Kakillioglu","year":"2020","unstructured":"Kakillioglu B, Ren A, Wang Y, Velipasalar S (2020) 3D capsule networks for object classification with weight pruning. IEEE Access 8:27393\u201327405","journal-title":"IEEE Access"},{"issue":"9","key":"6231_CR39","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1109\/83.623193","volume":"6","author":"Y Eldar","year":"1997","unstructured":"Eldar Y, Lindenbaum M, Porat M, Zeevi YY (1997) The farthest point strategy for progressive image sampling. IEEE Trans Image Process 6(9):1305\u20131315","journal-title":"IEEE Trans Image Process"},{"issue":"6088","key":"6231_CR40","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"key":"6231_CR41","doi-asserted-by":"crossref","unstructured":"Uy MA, Pham QH, Hua BS, Nguyen T, Yeung SK (2019) Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data. In: Proceedings of IEEE international conference on computer vision. Seoul, Korea (South), pp 1588\u20131597","DOI":"10.1109\/ICCV.2019.00167"},{"key":"6231_CR42","unstructured":"Loshchilov I, Hutter F (2017) SGDR: stochastic gradient descent with warm restarts. In: Proceedings of international conference on learning representations. Toulon, France"},{"key":"6231_CR43","doi-asserted-by":"crossref","unstructured":"Xu Y, Fan T, Xu M, Zeng L, Qiao Y (2018) SpiderCNN: deep learning on point sets with parameterized convolutional filters. In: Proceedings of European conference on computer vision. Munich, Germany, pp 87\u2013102","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"6231_CR44","doi-asserted-by":"crossref","unstructured":"Klokov R, Lempitsky V (2017) Escape from cells: deep kd-networks for the recognition of 3D point cloud models. In: Proceedings of IEEE international conference on computer vision. Venice, Italy, pp 863\u2013872","DOI":"10.1109\/ICCV.2017.99"},{"key":"6231_CR45","doi-asserted-by":"crossref","unstructured":"Li J, Chen BM, Hee Lee G (2018) So-Net: self-organizing network for point cloud analysis. In: Proceedings of IEEE conference on computer vision and pattern recognition. Salt Lake City, USA, pp 9397\u20139406","DOI":"10.1109\/CVPR.2018.00979"},{"key":"6231_CR46","doi-asserted-by":"crossref","unstructured":"Komarichev A, Zhong Z, Hua J (2019) A-CNN: annularly convolutional neural networks on point clouds. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 7421\u20137430","DOI":"10.1109\/CVPR.2019.00760"},{"key":"6231_CR47","doi-asserted-by":"crossref","unstructured":"Lan S, Yu R, Yu G, Davis LS (2019) Modeling local geometric structure of 3D point clouds using Geo-CNN. In: Proceedings of IEEE conference on computer vision and pattern recognition. Long Beach, USA, pp 998\u20131008","DOI":"10.1109\/CVPR.2019.00109"},{"key":"6231_CR48","doi-asserted-by":"crossref","unstructured":"Yan X, Zheng C, Li Z, Wang S, Cui S (2020) PointASNL: Robust point clouds processing using nonlocal neural networks with adaptive sampling. In: Proceedings of IEEE conference on computer vision and pattern recognition. Seattle, USA, pp 5589\u20135598","DOI":"10.1109\/CVPR42600.2020.00563"},{"issue":"11","key":"6231_CR49","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"6231_CR50","unstructured":"Ben-Shabat Y, Lindenbaum M, Fischer A (2017) 3D point cloud classification and segmentation using 3D modified fisher vector representation for convolutional neural networks, arXiv preprint arXiv:1711.08241"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06231-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06231-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06231-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T18:13:38Z","timestamp":1635963218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06231-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,26]]},"references-count":50,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6231"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06231-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,26]]},"assertion":[{"value":"17 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2021","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}