{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T22:45:26Z","timestamp":1752360326585,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030893699"},{"type":"electronic","value":"9783030893705"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89370-5_30","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"403-414","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MRAC-Net: Multi-resolution Anisotropic Convolutional Network for 3D Point Cloud Completion"],"prefix":"10.1007","author":[{"given":"Sheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingda","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"30_CR1","unstructured":"Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 40\u201349. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 10\u201315 July 2018. http:\/\/proceedings.mlr.press\/v80\/achlioptas18a.html"},{"key":"30_CR2","doi-asserted-by":"publisher","unstructured":"Berger, M., et al.: State of the art in surface reconstruction from point clouds. In: Eurographics 2014 - State of the Art Reports. EUROGRAPHICS Star Report, Strasbourg, France, vol. 1, pp. 161\u2013185, April 2014. https:\/\/doi.org\/10.2312\/egst.20141040. https:\/\/hal.inria.fr\/hal-01017700","DOI":"10.2312\/egst.20141040"},{"key":"30_CR3","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) (2016)"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Dai, A., Qi, C.R., Nie\u00dfner, M.: Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In: Proceedings of Computer Vision and Pattern Recognition (CVPR). IEEE (2017)","DOI":"10.1109\/CVPR.2017.693"},{"key":"30_CR5","doi-asserted-by":"publisher","unstructured":"Fan, H., Su, H., Guibas, L.: A point set generation network for 3D object reconstruction from a single image. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2463\u20132471 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.264","DOI":"10.1109\/CVPR.2017.264"},{"key":"30_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-01234-2_7","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Gadelha","year":"2018","unstructured":"Gadelha, M., Wang, R., Maji, S.: Multiresolution tree networks for 3D point cloud processing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 105\u2013122. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_7"},{"key":"30_CR7","unstructured":"Gao, Z., Zhai, G., Yan, J., Yang, X.: Permutation matters: anisotropic convolutional layer for learning on point clouds (2020)"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"Han, X., Li, Z., Huang, H., Kalogerakis, E., Yu, Y.: High-resolution shape completion using deep neural networks for global structure and local geometry inference. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 85\u201393 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.19","DOI":"10.1109\/ICCV.2017.19"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Hu, Q., et al.: RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"30_CR10","doi-asserted-by":"publisher","unstructured":"Hua, B., Tran, M., Yeung, S.: Pointwise convolutional neural networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 984\u2013993 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00109","DOI":"10.1109\/CVPR.2018.00109"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yu, Y., Xu, J., Ni, F., Le, X.: PF-Net: point fractal network for 3D point cloud completion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00768"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Lan, S., Yu, R., Yu, G., Davis, L.S.: Modeling local geometric structure of 3D point clouds using Geo-CNN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00109"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Lei, H., Akhtar, N., Mian, A.: Octree guided CNN with spherical kernels for 3D point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00986"},{"issue":"7","key":"30_CR14","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1109\/TVCG.2016.2553102","volume":"23","author":"D Li","year":"2017","unstructured":"Li, D., Shao, T., Wu, H., Zhou, K.: Shape completion from a single RGBD image. IEEE Trans. Vis. Comput. Graph. 23(7), 1809\u20131822 (2017). https:\/\/doi.org\/10.1109\/TVCG.2016.2553102","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"30_CR15","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on x-transformed points. In: Advances in Neural Information Processing Systems, pp. 820\u2013830 (2018)"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Li, Y., Dai, A., Guibas, L., Nie\u00dfner, M.: Database-assisted object retrieval for real-time 3D reconstruction. In: Computer Graphics Forum, vol. 34. Wiley Online Library (2015)","DOI":"10.1111\/cgf.12573"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3D object reconstruction. In: AAAI Conference on Artificial Intelligence (AAAI) (2018)","DOI":"10.1609\/aaai.v32i1.12278"},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Mao, J., Wang, X., Li, H.: Interpolated convolutional networks for 3D point cloud understanding. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00166"},{"key":"30_CR20","unstructured":"Martins, A.F.T., Astudillo, R.F.: From softmax to sparsemax: a sparse model of attention and multi-label classification. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML 2016, vol. 48, pp. 1614\u20131623 (2016). JMLR.org"},{"issue":"3","key":"30_CR21","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1145\/1141911.1141924","volume":"25","author":"NJ Mitra","year":"2006","unstructured":"Mitra, N.J., Guibas, L., Pauly, M.: Partial and approximate symmetry detection for 3D geometry. ACM Trans. Graph. (SIGGRAPH) 25(3), 560\u2013568 (2006)","journal-title":"ACM Trans. Graph. (SIGGRAPH)"},{"key":"30_CR22","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Sarmad, M., Lee, H.J., Kim, Y.M.: RL-GAN-Net: a reinforcement learning agent controlled GAN network for real-time point cloud shape completion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00605"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"issue":"9","key":"30_CR25","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1109\/TVCG.2019.2896310","volume":"26","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Lu, F.: VoxSegNet: volumetric CNNs for semantic part segmentation of 3D shapes. IEEE Trans. Vis. Comput. Graph. 26(9), 2919\u20132930 (2020). https:\/\/doi.org\/10.1109\/TVCG.2019.2896310","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9621\u20139630 (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"30_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-030-58545-7_21","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Xie","year":"2020","unstructured":"Xie, H., Yao, H., Zhou, S., Mao, J., Zhang, S., Sun, W.: GRNet: gridding residual network for dense point cloud completion. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 365\u2013381. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_21"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Yi, L., et al.: A scalable active framework for region annotation in 3D shape collections. SIGGRAPH Asia (2016)","DOI":"10.1145\/2980179.2980238"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: PCN: point completion network. In: 2018 International Conference on 3D Vision (3DV) (2018)","DOI":"10.1109\/3DV.2018.00088"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Birdal, T., Deng, H., Tombari, F.: 3D point capsule networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00110"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89370-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T06:46:11Z","timestamp":1673678771000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89370-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893699","9783030893705"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89370-5_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"382","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"93","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}