{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:25:41Z","timestamp":1743045941739,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031726231"},{"type":"electronic","value":"9783031726248"}],"license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72624-8_17","type":"book-chapter","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:52:13Z","timestamp":1729849933000},"page":"288-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Level Adaptive Self-labeling for\u00a0Novel Class Discovery in\u00a0Point Cloud Segmentation"],"prefix":"10.1007","author":[{"given":"Ruijie","family":"Xu","sequence":"first","affiliation":[]},{"given":"Chuyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Xuming","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"unstructured":"Asano, Y.M., Rupprecht, C., Vedaldi, A.: Self-labelling via simultaneous clustering and representation learning. In: International Conference on Learning Representations (ICLR) (2020)","key":"17_CR1"},{"issue":"8\u20139","key":"17_CR2","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1177\/02783649211006735","volume":"40","author":"J Behley","year":"2021","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Gall, J., Stachniss, C.: Towards 3d lidar-based semantic scene understanding of 3d point cloud sequences: the semantickitti dataset. Int. J. Robot. Res. 40(8\u20139), 959\u2013967 (2021)","journal-title":"Int. J. Robot. Res."},{"doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: Semantickitti: a dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9297\u20139307 (2019)","key":"17_CR3","DOI":"10.1109\/ICCV.2019.00939"},{"key":"17_CR4","first-page":"9912","volume":"33","author":"M Caron","year":"2020","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural. Inf. Process. Syst. 33, 9912\u20139924 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR5","first-page":"29512","volume":"35","author":"W Chang","year":"2022","unstructured":"Chang, W., Shi, Y., Tuan, H., Wang, J.: Unified optimal transport framework for universal domain adaptation. Adv. Neural. Inf. Process. Syst. 35, 29512\u201329524 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"314","key":"17_CR6","doi-asserted-by":"publisher","first-page":"2563","DOI":"10.1090\/mcom\/3303","volume":"87","author":"L Chizat","year":"2018","unstructured":"Chizat, L., Peyr\u00e9, G., Schmitzer, B., Vialard, F.X.: Scaling algorithms for unbalanced optimal transport problems. Math. Comput. 87(314), 2563\u20132609 (2018)","journal-title":"Math. Comput."},{"doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: minkowski convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3075\u20133084 (2019)","key":"17_CR7","DOI":"10.1109\/CVPR.2019.00319"},{"unstructured":"Cuturi, M.: Sinkhorn distances: Lightspeed computation of optimal transport. Advances in Neural Information Processing Systems 26 (2013)","key":"17_CR8"},{"unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et\u00a0al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD. vol.\u00a096, pp. 226\u2013231 (1996)","key":"17_CR9"},{"doi-asserted-by":"crossref","unstructured":"Fini, E., Sangineto, E., Lathuili\u00e8re, S., Zhong, Z., Nabi, M., Ricci, E.: A unified objective for novel class discovery. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9284\u20139292 (2021)","key":"17_CR10","DOI":"10.1109\/ICCV48922.2021.00915"},{"key":"17_CR11","first-page":"1","volume":"1","author":"R Flamary","year":"2016","unstructured":"Flamary, R., Courty, N., Tuia, D., Rakotomamonjy, A.: Optimal transport for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 1, 1\u201340 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361. IEEE (2012)","key":"17_CR12","DOI":"10.1109\/CVPR.2012.6248074"},{"doi-asserted-by":"crossref","unstructured":"Gu, P., Zhang, C., Xu, R., He, X.: Class-relation knowledge distillation for novel class discovery. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 16428\u201316437. IEEE Computer Society (2023)","key":"17_CR13","DOI":"10.1109\/ICCV51070.2023.01510"},{"doi-asserted-by":"crossref","unstructured":"Han, K., Rebuffi, S.A., Ehrhardt, S., Vedaldi, A., Zisserman, A.: Autonovel: automatically discovering and learning novel visual categories. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)","key":"17_CR14","DOI":"10.1109\/TPAMI.2021.3091944"},{"doi-asserted-by":"crossref","unstructured":"Han, K., Vedaldi, A., Zisserman, A.: Learning to discover novel visual categories via deep transfer clustering. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8401\u20138409 (2019)","key":"17_CR15","DOI":"10.1109\/ICCV.2019.00849"},{"doi-asserted-by":"crossref","unstructured":"Hu, Q., et al.: Randla-net: Efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11108\u201311117 (2020)","key":"17_CR16","DOI":"10.1109\/CVPR42600.2020.01112"},{"doi-asserted-by":"crossref","unstructured":"Lai, X., et al.: Stratified transformer for 3d point cloud segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8500\u20138509 (2022)","key":"17_CR17","DOI":"10.1109\/CVPR52688.2022.00831"},{"doi-asserted-by":"crossref","unstructured":"Lai, Z., Wang, C., Cheung, S.c., Chuah, C.N.: Sar: self-adaptive refinement on pseudo labels for multiclass-imbalanced semi-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4091\u20134100 (2022)","key":"17_CR18","DOI":"10.1109\/CVPRW56347.2022.00454"},{"doi-asserted-by":"crossref","unstructured":"Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558\u20134567 (2018)","key":"17_CR19","DOI":"10.1109\/CVPR.2018.00479"},{"unstructured":"Lee, D.H., et\u00a0al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML. vol.\u00a03, p.\u00a0896. Atlanta (2013)","key":"17_CR20"},{"issue":"8","key":"17_CR21","doi-asserted-by":"publisher","first-page":"3412","DOI":"10.1109\/TNNLS.2020.3015992","volume":"32","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: Deep learning for lidar point clouds in autonomous driving: a review. IEEE Trans. Neural Netw. Learn. Syst. 32(8), 3412\u20133432 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"doi-asserted-by":"publisher","unstructured":"Li, Z., et al.: Bevformer: learning bird\u2019s-eye-view representation from multi-camera images via spatiotemporal transformers. In: European Conference on Computer Vision, pp. 1\u201318. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20077-9_1","key":"17_CR22","DOI":"10.1007\/978-3-031-20077-9_1"},{"doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhou, Z., Sun, B.: Cot: Unsupervised domain adaptation with clustering and optimal transport. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19998\u201320007 (June 2023)","key":"17_CR23","DOI":"10.1109\/CVPR52729.2023.01915"},{"doi-asserted-by":"crossref","unstructured":"Long, F., Yao, Ting abd\u00a0Qiu, Z., Li, L., Mei, T.: Pointclustering: unsupervised point cloud pre-training using transformation invariance in clustering. In: CVPR (2023)","key":"17_CR24","DOI":"10.1109\/CVPR52729.2023.02090"},{"doi-asserted-by":"crossref","unstructured":"Nakajima, Y., Kang, B., Saito, H., Kitani, K.: Incremental class discovery for semantic segmentation with rgbd sensing. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 972\u2013981 (2019)","key":"17_CR25","DOI":"10.1109\/ICCV.2019.00106"},{"doi-asserted-by":"crossref","unstructured":"Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., Zhao, H.: Semanticposs: a point cloud dataset with large quantity of dynamic instances. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 687\u2013693. IEEE (2020)","key":"17_CR26","DOI":"10.1109\/IV47402.2020.9304596"},{"unstructured":"Phatak, A., Raghvendra, S., Tripathy, C., Zhang, K.: Computing all optimal partial transports. In: International Conference on Learning Representations (2023)","key":"17_CR27"},{"doi-asserted-by":"crossref","unstructured":"Riz, L., Saltori, C., Ricci, E., Poiesi, F.: Novel class discovery for 3d point cloud semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9393\u20139402 (2023)","key":"17_CR28","DOI":"10.1109\/CVPR52729.2023.00906"},{"issue":"7","key":"17_CR29","doi-asserted-by":"publisher","first-page":"6282","DOI":"10.1109\/TITS.2021.3086804","volume":"23","author":"R Roriz","year":"2021","unstructured":"Roriz, R., Cabral, J., Gomes, T.: Automotive lidar technology: a survey. IEEE Trans. Intell. Transp. Syst. 23(7), 6282\u20136297 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"17_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/978-3-030-58548-8_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"F Taherkhani","year":"2020","unstructured":"Taherkhani, F., Dabouei, A., Soleymani, S., Dawson, J., Nasrabadi, N.M.: Transporting labels via hierarchical optimal transport for semi-supervised learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 509\u2013526. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_30"},{"unstructured":"Tai, K.S., Bailis, P.D., Valiant, G.: Sinkhorn label allocation: semi-supervised classification via annealed self-training. In: International Conference on Machine Learning, pp. 10065\u201310075. PMLR (2021)","key":"17_CR31"},{"doi-asserted-by":"crossref","unstructured":"Vaze, S., Han, K., Vedaldi, A., Zisserman, A.: Generalized category discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7492\u20137501 (2022)","key":"17_CR32","DOI":"10.1109\/CVPR52688.2022.00734"},{"doi-asserted-by":"publisher","unstructured":"Villani, C., et\u00a0al.: Optimal transport: old and new, vol.\u00a0338. Springer (2009). https:\/\/doi.org\/10.1007\/978-3-540-71050-9","key":"17_CR33","DOI":"10.1007\/978-3-540-71050-9"},{"doi-asserted-by":"crossref","unstructured":"Yang, M., Zhu, Y., Yu, J., Wu, A., Deng, C.: Divide and conquer: compositional experts for generalized novel class discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14268\u201314277 (2022)","key":"17_CR34","DOI":"10.1109\/CVPR52688.2022.01387"},{"unstructured":"Zhang, C., Ren, H., He, X.: P$$^2$$ot: Progressive partial optimal transport for deep imbalanced clustering. In: The Twelfth International Conference on Learning Representations (2023)","key":"17_CR35"},{"unstructured":"Zhang, C., Xu, R., He, X.: Novel class discovery for long-tailed recognition. Transactions on Machine Learning Research (2023)","key":"17_CR36"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Khan, S., Shen, Z., Naseer, M., Chen, G., Khan, F.S.: Promptcal: contrastive affinity learning via auxiliary prompts for generalized novel category discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3479\u20133488 (2023)","key":"17_CR37","DOI":"10.1109\/CVPR52729.2023.00339"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Polarnet: an improved grid representation for online lidar point clouds semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9601\u20139610 (2020)","key":"17_CR38","DOI":"10.1109\/CVPR42600.2020.00962"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, B., Wang, B., Li, B.: Growsp: unsupervised semantic segmentation of 3d point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17619\u201317629 (2023)","key":"17_CR39","DOI":"10.1109\/CVPR52729.2023.01690"},{"unstructured":"Zhao, B., Han, K.: Novel visual category discovery with dual ranking statistics and mutual knowledge distillation. In: Advances in Neural Information Processing Systems, vol. 34 (2021)","key":"17_CR40"},{"doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhong, Z., Sebe, N., Lee, G.H.: Novel class discovery in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2022)","key":"17_CR41","DOI":"10.1109\/CVPR52688.2022.00430"},{"doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9939\u20139948 (2021)","key":"17_CR42","DOI":"10.1109\/CVPR46437.2021.00981"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72624-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T10:00:54Z","timestamp":1729850454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72624-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"ISBN":["9783031726231","9783031726248"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72624-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"26 October 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}