{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:58:01Z","timestamp":1772553481621,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200618","type":"print"},{"value":"9783031200625","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20062-5_27","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T10:31:55Z","timestamp":1668076315000},"page":"468-484","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Shape-Pose Disentanglement Using SE(3)-Equivariant Vector Neurons"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1514-8919","authenticated-orcid":false,"given":"Oren","family":"Katzir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6191-0361","authenticated-orcid":false,"given":"Dani","family":"Lischinski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6777-7445","authenticated-orcid":false,"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TAES.1975.308025","volume":"1","author":"IY Bar-Itzhack","year":"1975","unstructured":"Bar-Itzhack, I.Y.: Iterative optimal orthogonalization of the strapdown matrix. IEEE Trans. Aerosp. Electron. Syst. 1, 30\u201337 (1975)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"27_CR2","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"27_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46484-8_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"CB Choy","year":"2016","unstructured":"Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628\u2013644. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_38"},{"key":"27_CR4","unstructured":"Cohen, T.S., Geiger, M., K\u00f6hler, J., Welling, M.: Spherical CNNs. arXiv preprint arXiv:1801.10130 (2018)"},{"key":"27_CR5","unstructured":"Cohen, T.S., Welling, M.: Steerable CNNs. arXiv preprint arXiv:1612.08498 (2016)"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., Guibas, L.J.: Vector neurons: a general framework for SO(3)-equivariant networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12200\u201312209 (2021)","DOI":"10.1109\/ICCV48922.2021.01198"},{"key":"27_CR7","unstructured":"Deprelle, T., Groueix, T., Fisher, M., Kim, V., Russell, B., Aubry, M.: Learning elementary structures for 3D shape generation and matching. In: Advances in Neural Information Processing Systems, pp. 7433\u20137443 (2019)"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Esteves, C., Allen-Blanchette, C., Makadia, A., Daniilidis, K.: Learning SO(3) equivariant representations with spherical CNNs. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 52\u201368 (2018)","DOI":"10.1007\/978-3-030-01261-8_4"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A papier-m\u00e2ch\u00e9 approach to learning 3D surface generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 216\u2013224 (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"27_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-030-58558-7_17","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Gu","year":"2020","unstructured":"Gu, J., et al.: Weakly-supervised 3D shape completion in the wild. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 283\u2013299. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_17"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Hamdi, A., Giancola, S., Ghanem, B.: MVTN: multi-view transformation network for 3D shape recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1\u201311 (2021)","DOI":"10.1109\/ICCV48922.2021.00007"},{"issue":"3","key":"27_CR12","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s11263-012-0601-0","volume":"103","author":"R Hartley","year":"2013","unstructured":"Hartley, R., Trumpf, J., Dai, Y., Li, H.: Rotation averaging. Int. J. Comput. Vis. 103(3), 267\u2013305 (2013)","journal-title":"Int. J. Comput. Vis."},{"key":"27_CR13","unstructured":"Li, X., et al.: Leveraging SE(3) equivariance for self-supervised category-level object pose estimation from point clouds. In: Advances in Neural Information Processing Systems, vol. 34, pp. 15370\u201315381 (2021)"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhao, X., Huang, T., Hu, R., Zhou, Y., Bai, X.: TANet: robust 3D object detection from point clouds with triple attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11677\u201311684 (2020)","DOI":"10.1609\/aaai.v34i07.6837"},{"key":"27_CR15","unstructured":"Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: a simple residual MLP framework. In: International Conference on Learning Representations (2022)"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Novotny, D., Ravi, N., Graham, B., Neverova, N., Vedaldi, A.: C3DPO: canonical 3D pose networks for non-rigid structure from motion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7688\u20137697 (2019)","DOI":"10.1109\/ICCV.2019.00778"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"27_CR19","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, pp. 652\u2013660 (2017)"},{"key":"27_CR20","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"27_CR21","unstructured":"Rempe, D., Birdal, T., Zhao, Y., Gojcic, Z., Sridhar, S., Guibas, L.J.: CaSPR: learning canonical spatiotemporal point cloud representations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 13688\u201313701 (2020)"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Sajnani, R., Poulenard, A., Jain, J., Dua, R., Guibas, L.J., Sridhar, S.: ConDor: self-supervised canonicalization of 3d pose for partial shapes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16969\u201316979 (2022)","DOI":"10.1109\/CVPR52688.2022.01646"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., Li, H.: PointRCNN: 3D object proposal generation and detection from point cloud. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013779 (2019)","DOI":"10.1109\/CVPR.2019.00086"},{"key":"27_CR24","unstructured":"Spezialetti, R., Stella, F., Marcon, M., Silva, L., Salti, S., Di Stefano, L.: Learning to orient surfaces by self-supervised spherical CNNs. arXiv preprint arXiv:2011.03298 (2020)"},{"key":"27_CR25","unstructured":"Sun, W., et al.: Canonical capsules: self-supervised capsules in canonical pose. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"27_CR26","unstructured":"Thomas, N., et al.: Tensor field networks: rotation-and translation-equivariant neural networks for 3D point clouds. arXiv preprint arXiv:1802.08219 (2018)"},{"issue":"5","key":"27_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, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"27_CR28","unstructured":"Weiler, M., Geiger, M., Welling, M., Boomsma, W., Cohen, T.: 3D steerable CNNs: learning rotationally equivariant features in volumetric data. arXiv preprint arXiv:1807.02547 (2018)"},{"key":"27_CR29","unstructured":"Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: DISN: deep implicit surface network for high-quality single-view 3D reconstruction. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Yang, Y., Feng, C., Shen, Y., Tian, D.: FoldingNet: point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 206\u2013215 (2018)","DOI":"10.1109\/CVPR.2018.00029"},{"key":"27_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: point-based 3D single stage object detector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11040\u201311048 (2020)","DOI":"10.1109\/CVPR42600.2020.01105"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20062-5_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:17:10Z","timestamp":1668125830000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20062-5_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200618","9783031200625"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20062-5_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}