{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:12:39Z","timestamp":1732039959326},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1016\/j.neucom.2020.10.097","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T08:11:24Z","timestamp":1605168684000},"page":"94-103","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"special_numbering":"C","title":["3D-RVP: A method for 3D object reconstruction from a single depth view using voxel and point"],"prefix":"10.1016","volume":"430","author":[{"given":"Meihua","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Gang","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"MengChu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Fei-Yue","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2020.10.097_b0005","unstructured":"P. Achlioptas, O. Diamanti, I. Mitliagkas, L.J. Guibas, Learning representations and generative models for 3D point clouds. arXiv: Computer Vision and Pattern Recognition, 2017."},{"key":"10.1016\/j.neucom.2020.10.097_b0010","series-title":"International Conference on 3D Vision (3DV)","first-page":"412","article-title":"Hierarchical surface prediction for 3D object reconstruction","author":"Bane","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0015","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TEVC.2018.2885075","article-title":"Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions","volume":"23","author":"Cao","year":"2019","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"10.1016\/j.neucom.2020.10.097_b0020","unstructured":"A.X. Chang, T. Funkhouser, L.J. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al., Shapenet: an information-rich 3D model repository. arXiv: Graphics, 2015."},{"key":"10.1016\/j.neucom.2020.10.097_b0025","unstructured":"R.T.Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud, Neural ordinary differential equations, in: Advances in Neural Information Processing Systems (NIPS 2018), 2018."},{"key":"10.1016\/j.neucom.2020.10.097_b0030","doi-asserted-by":"crossref","unstructured":"C.B. Choy, D. Xu, J.Y. Gwak, K. Chen, S. Savarese, 3D\u2013R2N2: A unified approach for single and multi-view 3D object reconstruction, in: Computer Vision - ECCV 2016, PT VIII, 2016, pp. 628\u2013644. DOI: 10.1007\/978-3-319-46484-8_38.","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"10.1016\/j.neucom.2020.10.097_b0035","first-page":"424","article-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","author":"Cicek","year":"2016","journal-title":"Medical Image Computing and Computer Assisted Intervention"},{"key":"10.1016\/j.neucom.2020.10.097_b0040","series-title":"2018 International Conference on 3D Vision (3DV)","first-page":"79","article-title":"Progressive large-scale structure-from-motion with orthogonal MSTs","author":"Cui","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.097_b0045","series-title":"30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"6545","article-title":"Shape completion using 3D-encoder-predictor CNNs and shape synthesis","author":"Dai","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0050","doi-asserted-by":"crossref","unstructured":"H. Fan, H. Su, L. Guibas, A point set generation network for 3D object reconstruction from a single image, in: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017, pp. 2463\u20132471. DOI: 10.1109\/CVPR.2017.264.","DOI":"10.1109\/CVPR.2017.264"},{"key":"10.1016\/j.neucom.2020.10.097_b0055","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TNNLS.2018.2846646","article-title":"Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction","volume":"30","author":"Gao","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neucom.2020.10.097_b0060","doi-asserted-by":"crossref","first-page":"3766","DOI":"10.1109\/TITS.2019.2933509","article-title":"Can virtual samples solve small sample size problem of KISSME in pedestrian re-identification of smart transportation?","volume":"21","author":"Han","year":"2020","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.neucom.2020.10.097_b0065","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"85","article-title":"High-resolution shape completion using deep neural networks for global structure and local geometry inference","author":"Han","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0070","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Dollar, R. Girshick, Mask R-CNN, in: 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980\u20132988. DOI: 10.1109\/ICCV.2017.322.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.1016\/j.neucom.2020.10.097_b0075","series-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5353","article-title":"Convolutional neural networks at constrained time cost","author":"He","year":"2015"},{"key":"10.1016\/j.neucom.2020.10.097_b0080","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2020.10.097_b0085","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. van der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017, pp. 2261\u20132269. DOI: 10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.neucom.2020.10.097_b0090","first-page":"1","article-title":"An efficient group recommendation model with multiattention-based neural networks","author":"Huang","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neucom.2020.10.097_b0095","unstructured":"Z. Huang, Y. Yu, J. Xu, F. Ni, X. Le, PF-Net: Point fractal network for 3D point cloud completion. arXiv: Computer Vision and Pattern Recognition, 2020."},{"key":"10.1016\/j.neucom.2020.10.097_b0100","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/TCSS.2020.3001517","article-title":"Enhanced subspace distribution matching for fast visual domain adaptation","volume":"7","author":"Kang","year":"2020","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"10.1016\/j.neucom.2020.10.097_b0105","series-title":"2015 International Conference on Learning Representations (ICLR)","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.neucom.2020.10.097_b0110","unstructured":"A. Kirillov, Y. Wu, K. He, R. Girshick, PointRend: Image segmentation as rendering. arXiv: Computer Vision and Pattern Recognition, 2019."},{"key":"10.1016\/j.neucom.2020.10.097_b0115","series-title":"2017 International Conference on 3D Vision (3DV)","first-page":"126","article-title":"Interactive 3D modeling with a generative adversarial network","author":"Liu","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0120","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"8887","article-title":"Relation-shape convolutional neural network for point cloud analysis","author":"Liu","year":"2019"},{"key":"10.1016\/j.neucom.2020.10.097_b0125","first-page":"61","article-title":"A computer algorithm for reconstructing a scene from two projections","volume":"293","author":"Longuethiggins","year":"1987","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2020.10.097_b0130","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TCYB.2019.2903736","article-title":"Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors","volume":"50","author":"Luo","year":"2020","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.neucom.2020.10.097_b0135","series-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"9276","article-title":"Deep hough voting for 3D object detection in point clouds","author":"Qi","year":"2019"},{"key":"10.1016\/j.neucom.2020.10.097_b0140","series-title":"2018 IIEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"918","article-title":"Frustum pointNets for 3D object detection from RGB-D data","author":"Qi","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.097_b0145","doi-asserted-by":"crossref","unstructured":"C.R. Qi, H. Su, K. Mo, L.J. Guibas, PointNet: Deep learning on point sets for 3D classification and segmentation, in: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017, pp. 77\u201385. DOI: 10.1109\/CVPR.2017.16.","DOI":"10.1109\/CVPR.2017.16"},{"key":"10.1016\/j.neucom.2020.10.097_b0150","unstructured":"C.R. Qi, L. Yi, H. Su, L.J. Guibas, PointNet plus plus: Deep hierarchical feature learning on point sets in a metric space, in: Advances in Neural Information Processing Systems (NIPS 2017), 2017."},{"key":"10.1016\/j.neucom.2020.10.097_b0155","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.eng.2019.04.012","article-title":"Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives","volume":"5","author":"Qi","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.neucom.2020.10.097_b0160","unstructured":"D.J. Rezende, S.M.A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, N. Heess, Unsupervised learning of 3D structure from images, in: Advances in Neural Information Processing Systems (NIPS 2016), 2016."},{"key":"10.1016\/j.neucom.2020.10.097_b0165","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6620","article-title":"OctNet: Learning deep 3D representations at high resolutions","author":"Riegler","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0170","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer Assisted Intervention"},{"key":"10.1016\/j.neucom.2020.10.097_b0175","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/JAS.2018.7511189","article-title":"Randomized latent factor model for high-dimensional and sparse matrices from industrial applications","volume":"6","author":"Shang","year":"2019","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.neucom.2020.10.097_b0180","doi-asserted-by":"crossref","unstructured":"A. Sharma, O. Grau, M. Fritz, VConv-DAE: Deep volumetric shape learning without object labels, in: Computer Vision - ECCV 2016 Workshops, PT III, 2016, pp. 236\u2013250. DOI: 10.1007\/978-3-319-49409-8_20.","DOI":"10.1007\/978-3-319-49409-8_20"},{"key":"10.1016\/j.neucom.2020.10.097_b0185","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neucom.2020.10.097_b0190","series-title":"2017 Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"29","article-title":"Dynamic edge-conditioned filters in convolutional neural networks on graphs","author":"Simonovsky","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0195","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/JAS.2017.7510670","article-title":"A novel approach for enhancement of geometric and contrast resolution properties of low contrast images","volume":"5","author":"Singh","year":"2018","journal-title":"IEEE-CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.neucom.2020.10.097_b0200","series-title":"30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"190","article-title":"Semantic scene completion from a single depth image","author":"Song","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0205","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/JAS.2017.7510673","article-title":"Geographic, geometrical and semantic reconstruction of urban scene from high resolution oblique aerial images","volume":"6","author":"Sun","year":"2019","journal-title":"IEEE-CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.neucom.2020.10.097_b0210","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/ICCV.2017.230","article-title":"Octree Generating Networks: Efficient convolutional architectures for high-resolution 3D outputs","author":"Tatarchenko","year":"2017","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"10.1016\/j.neucom.2020.10.097_b0215","series-title":"International Conference on 3D Vision (3DV)","first-page":"537","article-title":"SEGCloud: Semantic segmentation of 3D point clouds","author":"Tchapmi","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0220","series-title":"2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","first-page":"2442","article-title":"Shape completion enabled robotic grasping","author":"Varley","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0225","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1109\/JAS.2020.1003117","article-title":"Avoiding non-Manhattan obstacles based on projection of spatial corners in indoor environment","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE-CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.neucom.2020.10.097_b0230","series-title":"2018 International Conference on 3D Vision (3DV)","first-page":"426","article-title":"Adversarial semantic scene completion from a single depth image","author":"Wang","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.097_b0235","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/JAS.2017.7510742","article-title":"From Mind to Products: Towards social manufacturing and service","volume":"5","author":"Xiong","year":"2018","journal-title":"IEEE-CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.neucom.2020.10.097_b0240","unstructured":"X. Yan, J. Yang, E. Yumer, Y. Guo, H. Lee, Perspective Transformer Nets: Learning single-view 3D object reconstruction without 3D supervision, in: Advances in Neural Information Processing Systems (NIPS 2016), 2016."},{"key":"10.1016\/j.neucom.2020.10.097_b0245","doi-asserted-by":"crossref","first-page":"2820","DOI":"10.1109\/TPAMI.2018.2868195","article-title":"Dense 3D object reconstruction from a single depth view","volume":"41","author":"Yang","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neucom.2020.10.097_b0250","doi-asserted-by":"crossref","unstructured":"B. Yang, H. Wen, S. Wang, R. Clark, A. Markham, N. Trigoni, 3D object reconstruction from a single depth view with adversarial learning, in: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW 2017), 2017, pp. 679\u2013688. DOI: 10.1109\/ICCVW.2017.86.","DOI":"10.1109\/ICCVW.2017.86"},{"key":"10.1016\/j.neucom.2020.10.097_b0255","series-title":"2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"206","article-title":"FoldingNet: Point cloud auto-encoder via deep grid deformation","author":"Yang","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.097_b0260","series-title":"2018 International Conference on 3D Vision (3DV)","first-page":"728","article-title":"PCN: Point completion network","author":"Yuan","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.097_b0265","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5560","article-title":"Pointweb: Enhancing local neighborhood features for point cloud processing","author":"Zhao","year":"2019"},{"key":"10.1016\/j.neucom.2020.10.097_b0270","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1912","article-title":"3D ShapeNets: A deep representation for volumetric shapes","author":"Zhirong","year":"2015"},{"key":"10.1016\/j.neucom.2020.10.097_b0275","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TMI.2019.2927436","article-title":"Real-time dense reconstruction of tissue surface from stereo optical video","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neucom.2020.10.097_b0280","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"57","article-title":"Rethinking Reprojection: Closing the loop for pose-aware shape reconstruction from a single image","author":"Zhu","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.097_b0285","series-title":"2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4568","article-title":"Very large-scale global SfM by distributed motion averaging","author":"Zhu","year":"2018"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220317100?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220317100?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T15:20:39Z","timestamp":1612884039000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231220317100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":57,"alternative-id":["S0925231220317100"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2020.10.097","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"3D-RVP: A method for 3D object reconstruction from a single depth view using voxel and point","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2020.10.097","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}