{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:33:39Z","timestamp":1742942019341,"version":"3.40.3"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200618"},{"type":"electronic","value":"9783031200625"}],"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_34","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T10:31:55Z","timestamp":1668076315000},"page":"593-609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SPE-Net: Boosting Point Cloud Analysis via\u00a0Rotation Robustness Enhancement"],"prefix":"10.1007","author":[{"given":"Zhaofan","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yehao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingwei","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Mei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"34_CR1","unstructured":"Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2D\u20133D-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017)"},{"key":"34_CR2","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Chen, C., Li, G., Xu, R., Chen, T., Wang, M., Lin, L.: ClusterNet: deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00513"},{"key":"34_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-030-01261-8_4","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Esteves","year":"2018","unstructured":"Esteves, C., Allen-Blanchette, C., Makadia, A., Daniilidis, K.: Learning SO(3) equivariant representations with spherical CNNs. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 54\u201370. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_4"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Fang, J., Zhou, D., Song, X., Jin, S., Yang, R., Zhang, L.: Rotpredictor: unsupervised canonical viewpoint learning for point cloud classification. In: 3DV (2020)","DOI":"10.1109\/3DV50981.2020.00109"},{"issue":"2","key":"34_CR6","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"MH Guo","year":"2021","unstructured":"Guo, M.H., Cai, J.X., Liu, Z.N., Mu, T.J., Martin, R.R., Hu, S.M.: PCT: point cloud transformer. Comput. Visual Media 7(2), 187\u2013199 (2021)","journal-title":"Comput. Visual Media"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"34_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3272127.3275110","volume":"37","author":"P Hermosilla","year":"2018","unstructured":"Hermosilla, P., Ritschel, T., V\u00e1zquez, P.P., Vinacua, \u00c0., Ropinski, T.: Monte Carlo convolution for learning on non-uniformly sampled point clouds. ACM Trans. Graphics 37(6), 1\u201312 (2018)","journal-title":"ACM Trans. Graphics"},{"key":"34_CR9","unstructured":"Kim, S., Park, J., Han, B.: Rotation-invariant local-to-global representation learning for 3D point cloud. In: NeurIPS (2020)"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Li, F., Fujiwara, K., Okura, F., Matsushita, Y.: A closer look at rotation-invariant deep point cloud analysis. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01591"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B.M., Lee, G.H.: SO-Net: self-organizing network for point cloud analysis. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00979"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Li, L., Zhu, S., Fu, H., Tan, P., Tai, C.L.: End-to-end learning local multi-view descriptors for 3D point clouds. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00199"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Li, R., Chen, G., Fu, C.W., Cohen-Or, D., Heng, P.A.: A rotation-invariant framework for deep point cloud analysis. IEEE Trans. Vis. Comput. Graphics (2021)","DOI":"10.1109\/TVCG.2021.3092570"},{"key":"34_CR14","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: NeurIPS (2018)"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Li, Y., Yao, T., Pan, Y., Mei, T.: Contextual transformer networks for visual recognition. IEEE Trans. PAMI (2022)","DOI":"10.1109\/TPAMI.2022.3164083"},{"issue":"8","key":"34_CR16","first-page":"4212","volume":"44","author":"ZH Lin","year":"2021","unstructured":"Lin, Z.H., Huang, S.Y., Wang, Y.C.F.: Learning of 3D graph convolution networks for point cloud analysis. IEEE Trans. PAMI 44(8), 4212\u20134224 (2021)","journal-title":"IEEE Trans. PAMI"},{"key":"34_CR17","unstructured":"Liu, M., Yao, F., Choi, C., Sinha, A., Ramani, K.: Deep learning 3D shapes using Alt-AZ anisotropic 2-sphere convolution. In: ICLR (2018)"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., Pan, C.: DensePoint: learning densely contextual representation for efficient point cloud processing. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00534"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"key":"34_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1007\/978-3-030-58592-1_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Hu, H., Cao, Y., Zhang, Z., Tong, X.: A closer look at local aggregation operators in point cloud analysis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 326\u2013342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_20"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Long, F., Qiu, Z., Pan, Y., Yao, T., Luo, J., Mei, T.: Stand-alone inter-frame attention in video models. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00319"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: IROS (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Mo, K., et al.: PartNet: a large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00100"},{"key":"34_CR24","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Poulenard, A., Rakotosaona, M.J., Ponty, Y., Ovsjanikov, M.: Effective rotation-invariant point CNN with spherical harmonics kernels. In: 3DV (2019)","DOI":"10.1109\/3DV.2019.00015"},{"key":"34_CR26","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)"},{"key":"34_CR27","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS (2017)"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., Ngo, C.W., Mei, T.: MLP-3D: a MLP-like 3D architecture with grouped time mixing. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00307"},{"key":"34_CR29","doi-asserted-by":"crossref","unstructured":"Rao, Y., Lu, J., Zhou, J.: Spherical fractal convolutional neural networks for point cloud recognition. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00054"},{"key":"34_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/978-3-030-58565-5_32","volume-title":"Computer Vision \u2013 ECCV 2020","author":"W Shen","year":"2020","unstructured":"Shen, W., Zhang, B., Huang, S., Wei, Z., Zhang, Q.: 3D-rotation-equivariant quaternion neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 531\u2013547. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_32"},{"key":"34_CR31","doi-asserted-by":"crossref","unstructured":"Shen, Y., Feng, C., Yang, Y., Tian, D.: Mining point cloud local structures by kernel correlation and graph pooling. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00478"},{"key":"34_CR32","doi-asserted-by":"crossref","unstructured":"Shi, S., et al.: PV-RCNN: point-voxel feature set abstraction for 3D object detection. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"34_CR33","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"34_CR34","doi-asserted-by":"crossref","unstructured":"Su, H., et al.: SPLATNet: sparse lattice networks for point cloud processing. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00268"},{"key":"34_CR35","doi-asserted-by":"crossref","unstructured":"Sun, X., Lian, Z., Xiao, J.: SRINet: learning strictly rotation-invariant representations for point cloud classification and segmentation. In: ACM MM (2019)","DOI":"10.1145\/3343031.3351042"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Tchapmi, L., Choy, C., Armeni, I., Gwak, J., Savarese, S.: SEGCloud: semantic segmentation of 3D point clouds. In: 3DV (2017)","DOI":"10.1109\/3DV.2017.00067"},{"key":"34_CR37","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: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"34_CR38","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In: ICML (2021)"},{"issue":"5","key":"34_CR39","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. Graphics 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graphics"},{"key":"34_CR40","unstructured":"Weiler, M., Geiger, M., Welling, M., Boomsma, W., Cohen, T.S.: 3D steerable CNNs: learning rotationally equivariant features in volumetric data. In: NeurIPS (2018)"},{"key":"34_CR41","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"34_CR42","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: CVPR (2015)"},{"key":"34_CR43","doi-asserted-by":"crossref","unstructured":"Xiao, C., Wachs, J.: Triangle-Net: towards robustness in point cloud learning. In: WACV (2021)","DOI":"10.1109\/WACV48630.2021.00087"},{"key":"34_CR44","doi-asserted-by":"crossref","unstructured":"Xu, J., Tang, X., Zhu, Y., Sun, J., Pu, S.: SGMNet: learning rotation-invariant point cloud representations via sorted gram matrix. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01030"},{"key":"34_CR45","doi-asserted-by":"crossref","unstructured":"Xu, M., Ding, R., Zhao, H., Qi, X.: PAConv: position adaptive convolution with dynamic kernel assembling on point clouds. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00319"},{"key":"34_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/978-3-030-01237-3_6","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Xu","year":"2018","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Yu.: SpiderCNN: deep learning on point sets with parameterized convolutional filters. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 90\u2013105. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_6"},{"key":"34_CR47","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Shen, X., Jia, J.: STD: sparse-to-dense 3D object detector for point cloud. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00204"},{"key":"34_CR48","doi-asserted-by":"crossref","unstructured":"You, H., Feng, Y., Ji, R., Gao, Y.: PVNet: a joint convolutional network of point cloud and multi-view for 3D shape recognition. In: ACM MM (2018)","DOI":"10.1145\/3240508.3240702"},{"key":"34_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-030-58607-2_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"R Yu","year":"2020","unstructured":"Yu, R., Wei, X., Tombari, F., Sun, J.: Deep positional and relational feature learning for rotation-invariant point cloud analysis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12355, pp. 217\u2013233. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_13"},{"key":"34_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, B.S., Rosen, D.W., Yeung, S.K.: Rotation invariant convolutions for 3D point clouds deep learning. In: 3DV (2019)","DOI":"10.1109\/3DV.2019.00031"},{"key":"34_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, B.S., Yeung, S.K.: ShellNet: efficient point cloud convolutional neural networks using concentric shells statistics. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00169"},{"key":"34_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Fu, C.W., Jia, J.: PointWeb: enhancing local neighborhood features for point cloud processing. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00571"},{"key":"34_CR53","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"34_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00472"}],"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_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:22:07Z","timestamp":1668126127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20062-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200618","9783031200625"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20062-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}