{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:50:37Z","timestamp":1742939437466,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984312"},{"type":"electronic","value":"9789819984329"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8432-9_10","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"115-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rotation-Invariant Completion Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2735-504X","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9463-6370","authenticated-orcid":false,"given":"Pengcheng","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"10_CR1","unstructured":"Qi, C.R., Su, H., Mo, K., et al.: 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\u2013660 (2017)"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., et al.: Pcn: point completion network. In: International Conference on 3D Vision (3DV), pp. 728\u2013737 (2018)","DOI":"10.1109\/3DV.2018.00088"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Liu, M., Sheng, L., Yang, S., et al.: Morphing and sampling network for dense point cloud completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11596\u201311603 (2020)","DOI":"10.1609\/aaai.v34i07.6827"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Pan, L.: Ecg: edge-aware point cloud completion with graph convolution. In: IEEE Robotics and Automation Letters (2020)","DOI":"10.1109\/LRA.2020.2994483"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Wang, X., Ang, M.H., Jr., Lee, G.H.: Cascaded refinement network for point cloud completion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 790\u2013799 (2020)","DOI":"10.1109\/CVPR42600.2020.00087"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Tchapmi, L.P., Kosaraju, V., Rezatofighi, H., et al.: Topnet: structural point cloud decoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 383\u2013392 (2019)","DOI":"10.1109\/CVPR.2019.00047"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Pan, L., Chen, X., Cai, Z., et al.: Variational relational point completion network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8524\u20138533 (2022)","DOI":"10.1109\/CVPR46437.2021.00842"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Zheng, C., Cham, T.-J., Cai, J.: Pluralistic image completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1438\u20131447 (2019)","DOI":"10.1109\/CVPR.2019.00153"},{"key":"10_CR9","unstructured":"Qi, C.R., Yi, L., Su, H., et al.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5105\u20135114 (2017)"},{"key":"10_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-642-15558-1_26","volume-title":"Computer Vision \u2013 ECCV 2010","author":"F Tombari","year":"2010","unstructured":"Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356\u2013369. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15558-1_26"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, B..S, Rosen, D.W., et al.: Rotation invariant convolutions for 3d point clouds deep learning. In: International Conference on 3D Vision, pp. 204\u2013213 (2019)","DOI":"10.1109\/3DV.2019.00031"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, B.S., Chen, W., et al.: Global context aware convolutions for 3d point cloud understanding. In: International Conference on 3D Vision, pp. 210\u2013219 (2020)","DOI":"10.1109\/3DV50981.2020.00031"},{"key":"10_CR13","unstructured":"Kim, S., Park, J., et al.: Rotation-invariant local-to-global representation learning for 3d point cloud. In: Advances in Neural Information Processing Systems, pp. 8174\u20138185 (2020)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Thomas, H.: Rotation-invariant point convolution with multiple equivariant alignments. In: 2020 International Conference on 3D Vision (3DV), pp. 504\u2013513 (2020)","DOI":"10.1109\/3DV50981.2020.00060"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Li, X., Li, et al.: A rotation-invariant framework for deep point cloud analysis. IEEE Trans. Visualizat. Comput. Graph. 4503\u20134514 (2021)","DOI":"10.1109\/TVCG.2021.3092570"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, S.K.: RIConv++: effective rotation invariant convolutions for 3D point clouds deep learning. Inter. J. Comput. Vis. 1228\u20131243 (2022)","DOI":"10.1007\/s11263-022-01601-z"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., et al.: Dynamic graph cnn for learning on point clouds. ACM Trans. Graph. 1\u201312 (2019)","DOI":"10.1145\/3326362"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Knapitsch, A., Park, J., Zhou, Q., et al.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph. (ToG), 1\u201313 (2017)","DOI":"10.1145\/3072959.3073599"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Richter, S.R., Ranftl, R., et al.: What do single-view 3d reconstruction networks learn? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3405\u20133414 (2019)","DOI":"10.1109\/CVPR.2019.00352"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Chen, B., Fan, J., Zhao, P., et al.: Slice sequential network: a lightweight unsupervised point cloud completion network. In: Pattern Recognition and Computer Vision: 4th Chinese Conference, PRCV 2021, pp. 103\u2013114 (2021)","DOI":"10.1007\/978-3-030-88007-1_9"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Tao, M., Zhao, C., Wang, J., et al.: Global patch cross-attention for point cloud analysis. In: Pattern Recognition and Computer Vision: 5th Chinese Conference, PRCV 2022, pp. 96\u2013111 (2022)","DOI":"10.1007\/978-3-031-18913-5_8"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Pan, L., Chen, X., Cai, Z., et al.: Variational relational point completion network for robust 3D classification. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3268305"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8432-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:04:12Z","timestamp":1703318652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8432-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984312","9789819984329"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8432-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"0","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":"37% - 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":"3,78","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":"3,69","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}