{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:59:18Z","timestamp":1742983158601,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984343"},{"type":"electronic","value":"9789819984350"}],"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\/978-981-99-8435-0_37","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"465-477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Modal and\u00a0Cross-Domain Knowledge Transfer for\u00a0Label-Free 3D Segmentation"],"prefix":"10.1007","author":[{"given":"Jingyu","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Huitong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Dai-Jie","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jacky","family":"Keung","sequence":"additional","affiliation":[]},{"given":"Xuesong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xinge","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yuexin","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: Semantickitti: a dataset for semantic scene understanding of lidar sequences. In: ICCV, pp. 9297\u20139307 (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Bian, Y., et al.: Unsupervised domain adaptation for point cloud semantic segmentation via graph matching. In: IROS, pp. 9899\u20139904. IEEE (2022)","DOI":"10.1109\/IROS47612.2022.9981603"},{"key":"37_CR3","doi-asserted-by":"publisher","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT\u20192010. Physica-Verlag HD, pp. 177\u2013186. Springer, Cham (2010). https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16","DOI":"10.1007\/978-3-7908-2604-3_16"},{"issue":"4","key":"37_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., et al.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. TPAMI 40(4), 834\u2013848 (2017)","journal-title":"TPAMI"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Cortinhal, T., et al.: SalsaNext: fast, uncertainty-aware semantic segmentation of lidar point clouds for autonomous driving (2020). arXiv:2003.03653","DOI":"10.1007\/978-3-030-64559-5_16"},{"issue":"6","key":"37_CR6","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., et al.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. CACM 24(6), 381\u2013395 (1981)","journal-title":"CACM"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Gerdzhev, M., et al.: Tornado-net: multiview total variation semantic segmentation with diamond inception module. In: ICRA, pp. 9543\u20139549. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9562041"},{"issue":"11","key":"37_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. CACM 63(11), 139\u2013144 (2020)","journal-title":"CACM"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Guo, X., et al.: SimT: handling open-set noise for domain adaptive semantic segmentation. In: CVPR, pp. 7032\u20137041 (2022)","DOI":"10.1109\/CVPR52688.2022.00690"},{"issue":"12","key":"37_CR10","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2020","unstructured":"Guo, Y., et al.: Deep learning for 3D point clouds: a survey. TPAMI 43(12), 4338\u20134364 (2020)","journal-title":"TPAMI"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Y., et al.: Point-to-voxel knowledge distillation for lidar semantic segmentation. In: CVPR, pp. 8479\u20138488 (2022)","DOI":"10.1109\/CVPR52688.2022.00829"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Category contrast for unsupervised domain adaptation in visual tasks. In: CVPR, pp. 1203\u20131214 (2022)","DOI":"10.1109\/CVPR52688.2022.00127"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Jaritz, M., et al.: xMUDA: cross-modal unsupervised domain adaptation for 3D semantic segmentation. In: CVPR, pp. 12605\u201312614 (2020)","DOI":"10.1109\/CVPR42600.2020.01262"},{"key":"37_CR14","unstructured":"Kingma, D.P., et al.: Adam: a method for stochastic optimization. arXiv preprint: arXiv:1412.6980 (2014)"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Langer, F., et al.: Domain transfer for semantic segmentation of LiDAR data using deep neural networks. In: IROS, pp. 8263\u20138270. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341508"},{"key":"37_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/978-3-030-58568-6_26","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Li","year":"2020","unstructured":"Li, G., Kang, G., Liu, W., Wei, Y., Yang, Y.: Content-consistent matching for domain adaptive semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 440\u2013456. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_26"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Li, W., et al.: SIGMA: semantic-complete graph matching for domain adaptive object detection. In: CVPR, pp. 5291\u20135300 (2022)","DOI":"10.1109\/CVPR52688.2022.00522"},{"key":"37_CR18","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TPAMI.2022.3179507","volume":"45","author":"Y Liao","year":"2022","unstructured":"Liao, Y., et al.: KITTI-360: a novel dataset and benchmarks for urban scene understanding in 2D and 3D. TPAMI 45, 3292\u20133310 (2022)","journal-title":"TPAMI"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Liu, M., et al.: Less: Label-efficient semantic segmentation for lidar point clouds. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. Lecture Notes in Computer Science, vol. 13699, pp. 70\u201389. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19842-7_5"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: One thing one click: a self-training approach for weakly supervised 3D semantic segmentation. In: CVPR, pp. 1726\u20131736 (2021)","DOI":"10.1109\/CVPR46437.2021.00177"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Milioto, A., et al.: RangeNet++: fast and accurate lidar semantic segmentation. In: IROS, pp. 4213\u20134220. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"37_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/978-3-030-58545-7_33","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Paul","year":"2020","unstructured":"Paul, S., Tsai, Y.-H., Schulter, S., Roy-Chowdhury, A.K., Chandraker, M.: Domain adaptive semantic segmentation using weak labels. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 571\u2013587. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_33"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Peng, X., et al.: CL3D: unsupervised domain adaptation for cross-LiDAR 3D detection (2022). arXiv:2212.00244","DOI":"10.1609\/aaai.v37i2.25297"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Ren, Z., et al.: 3D spatial recognition without spatially labeled 3D. In: CVPR, pp. 13204\u201313213 (2021)","DOI":"10.1109\/CVPR46437.2021.01300"},{"key":"37_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-319-46475-6_7","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SR Richter","year":"2016","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102\u2013118. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7"},{"key":"37_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Sautier, C., et al.: Image-to-lidar self-supervised distillation for autonomous driving data. In: CVPR. pp. 9891\u20139901 (2022)","DOI":"10.1109\/CVPR52688.2022.00966"},{"key":"37_CR28","doi-asserted-by":"crossref","unstructured":"Shi, H., et al.: Weakly supervised segmentation on outdoor 4D point clouds with temporal matching and spatial graph propagation. In: CVPR, pp. 11840\u201311849 (2022)","DOI":"10.1109\/CVPR52688.2022.01154"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Tranheden, W., et al.: DACS: domain adaptation via cross-domain mixed sampling. In: WACV, pp. 1379\u20131389 (2021)","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Unal, O., et al.: Scribble-supervised lidar semantic segmentation. In: CVPR, pp. 2697\u20132707 (2022)","DOI":"10.1109\/CVPR52688.2022.00272"},{"key":"37_CR31","doi-asserted-by":"crossref","unstructured":"Vu, T.H., et al.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: CVPR, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"issue":"5","key":"37_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., et al.: Dynamic graph CNN for learning on point clouds. TOG 38(5), 1\u201312 (2019)","journal-title":"TOG"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Wu, B., et al.: SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In: ICRA, pp. 1887\u20131893. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Wu, X., et al.: DanNet: a one-stage domain adaptation network for unsupervised nighttime semantic segmentation. In: CVPR, pp. 15769\u201315778 (2021)","DOI":"10.1109\/CVPR46437.2021.01551"},{"key":"37_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-031-19815-1_39","volume-title":"Computer Vision - ECCV 2022","author":"X Yan","year":"2022","unstructured":"Yan, X., et al.: 2DPASS: 2D priors assisted semantic segmentation on LiDAR point clouds. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. Lecture Notes in Computer Science, vol. 13688, pp. 677\u2013695. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19815-1_39"},{"key":"37_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, P., et al.: Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In: CVPR, pp. 12414\u201312424 (2021)","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"37_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation. In: CVPR, pp. 9939\u20139948 (2021)","DOI":"10.1109\/CVPR46437.2021.00981"}],"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\/978-981-99-8435-0_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T19:34:00Z","timestamp":1730921640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8435-0_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984343","9789819984350"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8435-0_37","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)"}}]}}