{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:11:12Z","timestamp":1778080272367,"version":"3.51.4"},"publisher-location":"Cham","reference-count":69,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732317","type":"print"},{"value":"9783031732324","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"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\/978-3-031-73232-4_5","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:01:53Z","timestamp":1727589713000},"page":"81-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ItTakesTwo: Leveraging Peer Representations for\u00a0Semi-supervised LiDAR Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Yuyuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasileios","family":"Belagiannis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian","family":"Reid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gustavo","family":"Carneiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: 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)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Berman, M., Triki, A.R., Blaschko, M.B.: The lov\u00e1sz-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4413\u20134421 (2018)","DOI":"10.1109\/CVPR.2018.00464"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuscenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"5_CR5","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":"5_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, M., Hui, L., Xie, J., Yang, J.: Sspc-net: Semi-supervised semantic 3d point cloud segmentation network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 1140\u20131147 (2021)","DOI":"10.1609\/aaai.v35i2.16200"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Cheung, Y.m.: A rival penalized em algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol.\u00a04, pp. 633\u2013636. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1333852"},{"issue":"6","key":"5_CR9","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1109\/TKDE.2005.97","volume":"17","author":"YM Cheung","year":"2005","unstructured":"Cheung, Y.M.: Maximum weighted likelihood via rival penalized em for density mixture clustering with automatic model selection. IEEE Trans. Knowl. Data Eng. 17(6), 750\u2013761 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"issue":"1","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1\u201322 (1977)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Deng, S., Dong, Q., Liu, B., Hu, Z.: Superpoint-guided semi-supervised semantic segmentation of 3d point clouds. In: 2022 International conference on robotics and automation (ICRA), pp. 9214\u20139220. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9811904"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Fan, L., Xiong, X., Wang, F., Wang, N., Zhang, Z.: Rangedet: In defense of range view for lidar-based 3d object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2918\u20132927 (2021)","DOI":"10.1109\/ICCV48922.2021.00291"},{"key":"5_CR14","unstructured":"French, G., Aila, T., Laine, S., Mackiewicz, M., Finlayson, G.: Semi-supervised semantic segmentation needs strong, high-dimensional perturbations (2019)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., Van Der\u00a0Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9224\u20139232 (2018)","DOI":"10.1109\/CVPR.2018.00961"},{"issue":"3","key":"5_CR16","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1002\/rob.21918","volume":"37","author":"S Grigorescu","year":"2020","unstructured":"Grigorescu, S., Trasnea, B., Cocias, T., Macesanu, G.: A survey of deep learning techniques for autonomous driving. J. Field Rob. 37(3), 362\u2013386 (2020)","journal-title":"J. Field Rob."},{"issue":"12","key":"5_CR17","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4338\u20134364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Hou, J., Graham, B., Nie\u00dfner, M., Xie, S.: Exploring data-efficient 3D scene understanding with contrastive scene contexts. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15587\u201315597 (2021)","DOI":"10.1109\/CVPR46437.2021.01533"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Hou, Y., Zhu, X., Ma, Y., Loy, C.C., Li, Y.: Point-to-voxel knowledge distillation for lidar semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8479\u20138488 (2022)","DOI":"10.1109\/CVPR52688.2022.00829"},{"key":"5_CR22","unstructured":"Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., Yang, M.H.: Adversarial learning for semi-supervised semantic segmentation. arXiv preprint arXiv:1802.07934 (2018)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Jiang, L., et al.: Guided point contrastive learning for semi-supervised point cloud semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6423\u20136432 (2021)","DOI":"10.1109\/ICCV48922.2021.00636"},{"key":"5_CR24","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Kohli, A.P.S., Sitzmann, V., Wetzstein, G.: Semantic implicit neural scene representations with semi-supervised training. In: 2020 International Conference on 3D Vision (3DV), pp. 423\u2013433. IEEE (2020)","DOI":"10.1109\/3DV50981.2020.00052"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Kong, L., et al.: Rethinking range view representation for lidar segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 228\u2013240 (2023)","DOI":"10.1109\/ICCV51070.2023.00028"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Kong, L., Ren, J., Pan, L., Liu, Z.: Lasermix for semi-supervised lidar semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21705\u201321715 (2023)","DOI":"10.1109\/CVPR52729.2023.02079"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Lai, X., Chen, Y., Lu, F., Liu, J., Jia, J.: Spherical transformer for lidar-based 3D recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17545\u201317555 (2023)","DOI":"10.1109\/CVPR52729.2023.01683"},{"key":"5_CR29","doi-asserted-by":"publisher","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","volume":"8","author":"PH Le-Khac","year":"2020","unstructured":"Le-Khac, P.H., Healy, G., Smeaton, A.F.: Contrastive representation learning: a framework and review. IEEE Access 8, 193907\u2013193934 (2020)","journal-title":"IEEE Access"},{"key":"5_CR30","unstructured":"Li, J., Zhou, P., Xiong, C., Hoi, S.C.: Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966 (2020)"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Li, L., Shum, H.P., Breckon, T.P.: Less is more: reducing task and model complexity for 3d point cloud semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9361\u20139371 (2023)","DOI":"10.1109\/CVPR52729.2023.00903"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Li, M., et al.: Hybridcr: weakly-supervised 3d point cloud semantic segmentation via hybrid contrastive regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14930\u201314939 (2022)","DOI":"10.1109\/CVPR52688.2022.01451"},{"key":"5_CR33","first-page":"31360","volume":"35","author":"C Liang","year":"2022","unstructured":"Liang, C., Wang, W., Miao, J., Yang, Y.: Gmmseg: Gaussian mixture based generative semantic segmentation models. Adv. Neural. Inf. Process. Syst. 35, 31360\u201331375 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR34","first-page":"10421","volume":"35","author":"T Liang","year":"2022","unstructured":"Liang, T., et al.: Bevfusion: a simple and robust lidar-camera fusion framework. Adv. Neural. Inf. Process. Syst. 35, 10421\u201310434 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR35","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/978-3-031-19842-7_5","volume-title":"European Conference on Computer Vision","author":"M Liu","year":"2022","unstructured":"Liu, M., Zhou, Y., Qi, C.R., Gong, B., Su, H., Anguelov, D.: Less: label-efficient semantic segmentation for lidar point clouds. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) ECCV 202. LNCS, vol. 13699, pp. 70\u201389. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-19842-7_5"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Liu, W., Yue, X., Chen, Y., Denoeux, T.: Trusted multi-view deep learning with opinion aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 7585\u20137593 (2022)","DOI":"10.1609\/aaai.v36i7.20724"},{"key":"5_CR37","unstructured":"Liu, Y., Hu, Q., Lei, Y., Xu, K., Li, J., Guo, Y.: Box2seg: learning semantics of 3d point clouds with box-level supervision. arXiv preprint arXiv:2201.02963 (2022)"},{"key":"5_CR38","unstructured":"Liu, Y., et al.: Segment any point cloud sequences by distilling vision foundation models. arXiv preprint arXiv:2306.09347 (2023)"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Tian, Y., Chen, Y., Liu, F., Belagiannis, V., Carneiro, G.: Perturbed and strict mean teachers for semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4258\u20134267 (2022)","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Bevfusion: multi-task multi-sensor fusion with unified bird\u2019s-eye view representation. In: IEEE International Conference on Robotics and Automation (ICRA) (2023)","DOI":"10.1109\/ICRA48891.2023.10160968"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Bevfusion: multi-task multi-sensor fusion with unified bird\u2019s-eye view representation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 2774\u20132781. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160968"},{"key":"5_CR42","volume-title":"An Introduction to Information Retrieval","author":"CD Manning","year":"2009","unstructured":"Manning, C.D.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)"},{"key":"5_CR43","unstructured":"Mena, G., Nejatbakhsh, A., Varol, E., Niles-Weed, J.: Sinkhorn em: an expectation-maximization algorithm based on entropic optimal transport. arXiv preprint arXiv:2006.16548 (2020)"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Miech, A., Alayrac, J.B., Smaira, L., Laptev, I., Sivic, J., Zisserman, A.: End-to-end learning of visual representations from uncurated instructional videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9879\u20139889 (2020)","DOI":"10.1109\/CVPR42600.2020.00990"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: Rangenet++: Fast and accurate lidar semantic segmentation. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4213\u20134220. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"issue":"2","key":"5_CR46","doi-asserted-by":"publisher","first-page":"2116","DOI":"10.1109\/LRA.2022.3142440","volume":"7","author":"L Nunes","year":"2022","unstructured":"Nunes, L., Marcuzzi, R., Chen, X., Behley, J., Stachniss, C.: Segcontrast: 3d point cloud feature representation learning through self-supervised segment discrimination. IEEE Rob. Autom. Lett. 7(2), 2116\u20132123 (2022)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12674\u201312684 (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"5_CR48","doi-asserted-by":"crossref","unstructured":"Reichardt, L., Ebert, N., Wasenm\u00fcller, O.: 360deg from a single camera: a few-shot approach for lidar segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1075\u20131083 (2023)","DOI":"10.1109\/ICCVW60793.2023.00115"},{"issue":"2","key":"5_CR49","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s41095-022-0281-9","volume":"9","author":"CY Sun","year":"2023","unstructured":"Sun, C.Y., et al.: Semi-supervised 3d shape segmentation with multilevel consistency and part substitution. Comput. Visual Media 9(2), 229\u2013247 (2023)","journal-title":"Comput. Visual Media"},{"key":"5_CR50","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. arXiv preprint arXiv:1703.01780 (2017)"},{"key":"5_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1007\/978-3-030-58621-8_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 776\u2013794. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_45"},{"key":"5_CR52","doi-asserted-by":"crossref","unstructured":"Unal, O., Dai, D., Van\u00a0Gool, L.: Scribble-supervised lidar semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2697\u20132707 (2022)","DOI":"10.1109\/CVPR52688.2022.00272"},{"key":"5_CR53","unstructured":"Vora, S., et al.: Nesf: neural semantic fields for generalizable semantic segmentation of 3d scenes. arXiv preprint arXiv:2111.13260 (2021)"},{"key":"5_CR54","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., Van\u00a0Gool, L.: Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7303\u20137313 (2021)","DOI":"10.1109\/ICCV48922.2021.00721"},{"key":"5_CR55","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., Keutzer, K.: Squeezeseg: convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1887\u20131893. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"5_CR56","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/978-3-030-58580-8_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Xie","year":"2020","unstructured":"Xie, S., Gu, J., Guo, D., Qi, C.R., Guibas, L., Litany, O.: PointContrast: unsupervised pre-training for 3D point cloud understanding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 574\u2013591. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_34"},{"key":"5_CR57","unstructured":"Xu, J., Hsu, D.J., Maleki, A.: Benefits of over-parameterization with em. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"5_CR58","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: Rpvnet: a deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16024\u201316033 (2021)","DOI":"10.1109\/ICCV48922.2021.01572"},{"key":"5_CR59","doi-asserted-by":"crossref","unstructured":"Xu, J., Tang, H., Ren, Y., Peng, L., Zhu, X., He, L.: Multi-level feature learning for contrastive multi-view clustering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16051\u201316060 (2022)","DOI":"10.1109\/CVPR52688.2022.01558"},{"key":"5_CR60","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yuan, B., Zhao, S., Zhang, Q., Gao, X.: Hierarchical point-based active learning for semi-supervised point cloud semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 18098\u201318108 (2023)","DOI":"10.1109\/ICCV51070.2023.01659"},{"key":"5_CR61","doi-asserted-by":"crossref","unstructured":"Yang, L., Qi, L., Feng, L., Zhang, W., Shi, Y.: Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7236\u20137246 (2023)","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"5_CR62","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., Gao, Y.: St++: Make self-training work better for semi-supervised semantic segmentation. arXiv preprint arXiv:2106.05095 (2021)","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"5_CR63","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"5_CR64","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)","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"5_CR65","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Bai, L., Huang, X.: Fidnet: lidar point cloud semantic segmentation with fully interpolation decoding. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4453\u20134458. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636385"},{"key":"5_CR66","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)","DOI":"10.1109\/CVPR46437.2021.00981"},{"key":"5_CR67","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., Tan, M.: Perception-aware multi-sensor fusion for 3d lidar semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16280\u201316290 (2021)","DOI":"10.1109\/ICCV48922.2021.01597"},{"key":"5_CR68","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 289\u2013305 (2018)","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"5_CR69","unstructured":"Zou, Y., et al.: Pseudoseg: designing pseudo labels for semantic segmentation. arXiv preprint arXiv:2010.09713 (2020)"}],"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\/978-3-031-73232-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:03:18Z","timestamp":1727589798000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73232-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031732317","9783031732324"],"references-count":69,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73232-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 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"}}]}}