{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:52:45Z","timestamp":1780635165434,"version":"3.54.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030586065","type":"print"},{"value":"9783030586072","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58607-2_35","type":"book-chapter","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T08:04:11Z","timestamp":1604649851000},"page":"600-616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["CN: Channel Normalization for Point Cloud Recognition"],"prefix":"10.1007","author":[{"given":"Zetong","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojuan","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaya","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,7]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Q., Sun, L., Wang, Z., Jia, K., Yuille, A.: Object as hotspots: an anchor-free 3D object detection approach via firing of hotspots (2019)","DOI":"10.1007\/978-3-030-58589-1_5"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Shen, X., Jia, J.: Fast point R-CNN. In: Proceedings of the ICCV (2019)","DOI":"10.1109\/ICCV.2019.00987"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhang, Z., Zhao, X., Ji, R., Gao, Y.: GVCNN: group-view convolutional neural networks for 3D shape recognition. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00035"},{"key":"35_CR4","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32, 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.1109\/TIP.2016.2609814","volume":"25","author":"H Guo","year":"2016","unstructured":"Guo, H., Wang, J., Gao, Y., Li, J., Lu, H.: Multi-view 3D object retrieval with deep embedding network. IEEE Trans. Image Process. 25, 5526\u20135537 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Klokov, R., Lempitsky, V.S.: Escape from cells: deep Kd-networks for the recognition of 3D point cloud models. In: Proceedings of the ICCV (2017)","DOI":"10.1109\/ICCV.2017.99"},{"key":"35_CR7","doi-asserted-by":"publisher","first-page":"704","DOI":"10.3390\/s20030704","volume":"20","author":"H Kuang","year":"2020","unstructured":"Kuang, H., Wang, B., An, J., Zhang, M., Zhang, Z.: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. Sensors 20, 704 (2020)","journal-title":"Sensors"},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: Proceedings of the CVPR (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Li, R., Li, X., Fu, C., Cohen-Or, D., Heng, P.: PU-GAN: a point cloud upsampling adversarial network. CoRR (2019)","DOI":"10.1109\/ICCV.2019.00730"},{"key":"35_CR10","unstructured":"Li, Y., Bu, R., Sun, M., Chen, B.: PointCNN. CoRR (2018)"},{"key":"35_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/978-3-030-01270-0_39","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Liang","year":"2018","unstructured":"Liang, M., Yang, B., Wang, S., Urtasun, R.: Deep continuous fusion for multi-sensor 3D object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 663\u2013678. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_39"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Liu, X., Qi, C.R., Guibas, L.J.: FlowNet3D: learning scene flow in 3D point clouds. In: Proceedings of the CVPR (2019)","DOI":"10.1109\/CVPR.2019.00062"},{"key":"35_CR13","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: Proceedings of the ICCV (2019)","DOI":"10.1109\/ICCV.2019.00534"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the CVPR (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"key":"35_CR15","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. AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6837"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Mao, J., Wang, X., Li, H.: Interpolated convolutional networks for 3D point cloud understanding. In: Proceedings of the ICCV (2019)","DOI":"10.1109\/ICCV.2019.00166"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: Proceedings of the IROS (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3D object detection from RGB-D data. CoRR (2017)","DOI":"10.1109\/CVPR.2018.00102"},{"key":"35_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 CVPR (2017)"},{"key":"35_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: Proceedings of the NIPS (2017)"},{"key":"35_CR21","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: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00478"},{"key":"35_CR22","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 CVPR (2019)","DOI":"10.1109\/CVPR.2019.00086"},{"key":"35_CR23","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Su, H., et al.: SPLATNet: sparse lattice networks for point cloud processing. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00268"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the ICCV (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the ICCV (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"35_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-030-01225-0_4","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Wang","year":"2018","unstructured":"Wang, C., Samari, B., Siddiqi, K.: Local spectral graph convolution for point set feature learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 56\u201371. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_4"},{"key":"35_CR28","first-page":"1","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. 38, 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Li, F.: PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of the CVPR (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"35_CR30","unstructured":"Wu, Z., et al.: 3D shapeNets: a deep representation for volumetric shapes. In: Proceedings of the CVPR (2015)"},{"key":"35_CR31","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.3390\/s18103337","volume":"18","author":"Y Yan","year":"2018","unstructured":"Yan, Y., Mao, Y., Li, B.: Second: sparsely embedded convolutional detection. Sensors 18, 3337 (2018)","journal-title":"Sensors"},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: point-based 3D single stage object detector (2020)","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"35_CR33","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: Proceedings of the ICCV (2019)","DOI":"10.1109\/ICCV.2019.00204"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58607-2_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:49:33Z","timestamp":1730854173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58607-2_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586065","9783030586072"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58607-2_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}