{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T02:04:56Z","timestamp":1778897096207,"version":"3.51.4"},"publisher-location":"Cham","reference-count":120,"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_4","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:01:53Z","timestamp":1727589713000},"page":"58-80","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["4D Contrastive Superflows are Dense 3D Representation Learners"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingdong","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Shuai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"issue":"11","key":"4_CR1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Aygun, M., et al.: 4D panoptic lidar segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5527\u20135537 (2021)","DOI":"10.1109\/CVPR46437.2021.00548"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Badue, C., et al.: Self-driving cars: a survey. Expert Syst. Appl. 165, 113816 (2021)","DOI":"10.1016\/j.eswa.2020.113816"},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1177\/02783649211006735","volume":"40","author":"J Behley","year":"2021","unstructured":"Behley, J., et al.: Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: the SemanticKITTI dataset. Int. J. Robot. Res. 40, 959\u201396 (2021)","journal-title":"Int. J. Robot. Res."},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of lidar sequences. In: IEEE\/CVF International Conference on Computer Vision, pp. 9297\u20139307 (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"issue":"8","key":"4_CR6","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Boulch, A., Sautier, C., Michele, B., Puy, G., Marlet, R.: ALSO: automotive Lidar self-supervision by occupancy estimation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13455\u201313465 (2023)","DOI":"10.1109\/CVPR52729.2023.01293"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Cao, A.Q., Dai, A., de\u00a0Charette, R.: PaSCo: urban 3D panoptic scene completion with uncertainty awareness. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14554\u201314564 (2024)","DOI":"10.1109\/CVPR52733.2024.01379"},{"key":"4_CR10","unstructured":"Chen, Q., Vora, S., Beijbom, O.: PolarStream: streaming Lidar object detection and segmentation with polar pillars. In: Advances in Neural Information Processing Systems, vol.\u00a034 (2021)"},{"key":"4_CR11","unstructured":"Chen, R., et al.: Towards label-free scene understanding by vision foundation models. In: Advances in Neural Information Processing Systems, vol.\u00a036 (2023)"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Chen, R., et al.: CLIP2Scene: towards label-efficient 3D scene understanding by clip. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7020\u20137030 (2023)","DOI":"10.1109\/CVPR52729.2023.00678"},{"key":"4_CR13","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 (2020)"},{"key":"4_CR14","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: IEEE\/CVF International Conference on Computer Vision, pp. 9640\u20139649 (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Chen, Y., Nie\u00dfner, M., Dai, A.: 4DContrast: contrastive learning with dynamic correspondences for 3D scene understanding. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13692, pp. 543\u2013560. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19824-3_32","DOI":"10.1007\/978-3-031-19824-3_32"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Cheng, H., Han, X., Xiao, G.: CENet: toward concise and efficient LiDAR semantic segmentation for autonomous driving. In: IEEE International Conference on Multimedia and Expo, pp.\u00a01\u20136 (2022)","DOI":"10.1109\/ICME52920.2022.9859693"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Cheng, R., Razani, R., Taghavi, E., Li, E., Liu, B.: AF2-S3Net: attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12547\u201312556 (2021)","DOI":"10.1109\/CVPR46437.2021.01236"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: Minkowski convolutional neural networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3075\u20133084 (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"4_CR20","unstructured":"Contributors, M.: MMDetection3D: OpenMMLab next-generation platform for general 3D object detection (2020). https:\/\/github.com\/open-mmlab\/mmdetection3d"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Cortinhal, T., Tzelepis, G., Aksoy, E.E.: SalsaNext: fast, uncertainty-aware semantic segmentation of LiDAR point clouds. In: International Symposium on Visual Computing, pp. 207\u2013222 (2020)","DOI":"10.1007\/978-3-030-64559-5_16"},{"key":"4_CR22","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Duerr, F., Pfaller, M., Weigel, H., Beyerer, J.: LiDAR-based recurrent 3D semantic segmentation with temporal memory alignment. In: International Conference on 3D Vision, pp. 781\u2013790 (2020)","DOI":"10.1109\/3DV50981.2020.00088"},{"key":"4_CR24","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 226\u2013231 (1996)"},{"issue":"6","key":"4_CR25","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM"},{"key":"4_CR26","doi-asserted-by":"publisher","first-page":"3795","DOI":"10.1109\/LRA.2022.3148457","volume":"7","author":"WK Fong","year":"2022","unstructured":"Fong, W.K., et al.: Panoptic nuScenes: a large-scale benchmark for LiDAR Panoptic segmentation and tracking. IEEE Robot. Autom. Lett. 7, 3795\u20133802 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"7","key":"4_CR27","doi-asserted-by":"publisher","first-page":"6063","DOI":"10.1109\/TITS.2021.3076844","volume":"23","author":"B Gao","year":"2021","unstructured":"Gao, B., Pan, Y., Li, C., Geng, S., Zhao, H.: Are we hungry for 3D LiDAR data for semantic segmentation? A survey of datasets and methods. IEEE Trans. Intell. Transp. Syst. 23(7), 6063\u20136081 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"4_CR29","unstructured":"Hao, X., et al.: Is your HD map constructor reliable under sensor corruptions? arXiv preprint arXiv:2406.12214 (2024)"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Hess, G., Jaxing, J., Svensson, E., Hagerman, D., Petersson, C., Svensson, L.: Masked autoencoders for self-supervised learning on automotive point clouds. arXiv preprint arXiv:2207.00531 (2022)","DOI":"10.1109\/WACVW58289.2023.00039"},{"issue":"5","key":"4_CR33","doi-asserted-by":"publisher","first-page":"3480","DOI":"10.1109\/TPAMI.2023.3349304","volume":"46","author":"F Hong","year":"2024","unstructured":"Hong, F., Kong, L., Zhou, H., Zhu, X., Li, H., Liu, Z.: Unified 3D and 4D Panoptic segmentation via dynamic shifting networks. IEEE Trans. Pattern Anal. Mach. Intell. 46(5), 3480\u20133495 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR34","doi-asserted-by":"crossref","unstructured":"Hong, F., Zhou, H., Zhu, X., Li, H., Liu, Z.: LiDAR-based Panoptic segmentation via dynamic shifting network. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13090\u201313099 (2021)","DOI":"10.1109\/CVPR46437.2021.01289"},{"key":"4_CR35","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: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15587\u201315597 (2021)","DOI":"10.1109\/CVPR46437.2021.01533"},{"key":"4_CR36","doi-asserted-by":"publisher","unstructured":"Hu, Q., et al.: SQN: weakly-supervised semantic segmentation of large-scale 3D point clouds. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13687, pp. 600\u2013619. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19812-0_35","DOI":"10.1007\/978-3-031-19812-0_35"},{"key":"4_CR37","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., Markham, A.: Towards semantic segmentation of urban-scale 3D point clouds: a dataset, benchmarks and challenges. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4977\u20134987 (2021)","DOI":"10.1109\/CVPR46437.2021.00494"},{"key":"4_CR38","doi-asserted-by":"publisher","unstructured":"Hu, Z., et al.: LiDAL: inter-frame uncertainty based active learning for 3D LiDAR semantic segmentation. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13687, pp. 248\u2013265. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19812-0_15","DOI":"10.1007\/978-3-031-19812-0_15"},{"key":"4_CR39","doi-asserted-by":"crossref","unstructured":"Huang, S., Xie, Y., Zhu, S.C., Zhu, Y.: Spatio-temporal self-supervised representation learning for 3D point clouds. In: IEEE\/CVF International Conference on Computer Vision, pp. 6535\u20136545 (2021)","DOI":"10.1109\/ICCV48922.2021.00647"},{"key":"4_CR40","doi-asserted-by":"crossref","unstructured":"Jaritz, M., Vu, T.H., de\u00a0Charette, R., Wirbel, E., P\u00e9rez, P.: xMUDA: cross-modal unsupervised domain adaptation for 3D semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12605\u201312614 (2020)","DOI":"10.1109\/CVPR42600.2020.01262"},{"key":"4_CR41","doi-asserted-by":"crossref","unstructured":"Jiang, P., Osteen, P., Wigness, M., Saripallig, S.: RELLIS-3D dataset: data, benchmarks and analysis. In: IEEE International Conference on Robotics and Automation, pp. 1110\u20131116 (2021)","DOI":"10.1109\/ICRA48506.2021.9561251"},{"key":"4_CR42","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"4_CR43","doi-asserted-by":"publisher","first-page":"79341","DOI":"10.1109\/ACCESS.2023.3298706","volume":"11","author":"A Klokov","year":"2023","unstructured":"Klokov, A., et al.: DAPS3D: domain adaptive projective segmentation of 3D LiDAR point clouds. IEEE Access 11, 79341\u201379356 (2023)","journal-title":"IEEE Access"},{"key":"4_CR44","doi-asserted-by":"crossref","unstructured":"Kong, L., et al.: Rethinking range view representation for LiDAR segmentation. In: IEEE\/CVF International Conference on Computer Vision, pp. 228\u2013240 (2023)","DOI":"10.1109\/ICCV51070.2023.00028"},{"key":"4_CR45","doi-asserted-by":"crossref","unstructured":"Kong, L., et al.: Robo3D: towards robust and reliable 3D perception against corruptions. In: IEEE\/CVF International Conference on Computer Vision, pp. 19994\u201320006 (2023)","DOI":"10.1109\/ICCV51070.2023.01830"},{"key":"4_CR46","doi-asserted-by":"crossref","unstructured":"Kong, L., Quader, N., Liong, V.E.: ConDA: unsupervised domain adaptation for LiDAR segmentation via regularized domain concatenation. In: IEEE International Conference on Robotics and Automation, pp. 9338\u20139345 (2023)","DOI":"10.1109\/ICRA48891.2023.10160410"},{"key":"4_CR47","doi-asserted-by":"crossref","unstructured":"Kong, L., Ren, J., Pan, L., Liu, Z.: LaserMix for semi-supervised lidar semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21705\u201321715 (2023)","DOI":"10.1109\/CVPR52729.2023.02079"},{"key":"4_CR48","unstructured":"Kong, L., Xie, S., Hu, H., Ng, L.X., Cottereau, B.R., Ooi, W.T.: RoboDepth: robust out-of-distribution depth estimation under corruptions. In: Advances in Neural Information Processing Systems, vol.\u00a036 (2023)"},{"key":"4_CR49","unstructured":"Kong, L., et al.: Multi-modal data-efficient 3D scene understanding for autonomous driving. arXiv preprint arXiv:2405.05258 (2024)"},{"key":"4_CR50","doi-asserted-by":"crossref","unstructured":"Krispel, G., Schinagl, D., Fruhwirth-Reisinger, C., Possegger, H., Bischof, H.: MAELi: masked autoencoder for large-scale LiDAR point clouds. In: IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3383\u20133392 (2024)","DOI":"10.1109\/WACV57701.2024.00335"},{"key":"4_CR51","first-page":"193907","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 Trans. Pattern Anal. Mach. Intell. 8, 193907\u2013193934 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR52","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: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9361\u20139371 (2023)","DOI":"10.1109\/CVPR52729.2023.00903"},{"key":"4_CR53","unstructured":"Li, R., de\u00a0Charette, R., Cao, A.Q.: Coarse3D: class-prototypes for contrastive learning in weakly-supervised 3D point cloud segmentation. In: British Machine Vision Conference (2022)"},{"key":"4_CR54","unstructured":"Li, Y., Kong, L., Hu, H., Xu, X., Huang, X.: Optimizing LiDAR placements for robust driving perception in adverse conditions. arXiv preprint arXiv:2403.17009 (2024)"},{"issue":"4","key":"4_CR55","doi-asserted-by":"publisher","first-page":"6458","DOI":"10.1109\/LRA.2021.3093009","volume":"6","author":"H Lim","year":"2021","unstructured":"Lim, H., Oh, M., Myung, H.: Patchwork: concentric zone-based region-wise ground segmentation with ground likelihood estimation using a 3D LiDAR sensor. IEEE Robot. Autom. Lett. 6(4), 6458\u20136465 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"4_CR56","unstructured":"Liong, V.E., Nguyen, T.N.T., Widjaja, S., Sharma, D., Chong, Z.J.: AMVNet: assertion-based multi-view fusion network for LiDAR semantic segmentation. arXiv preprint arXiv:2012.04934 (2020)"},{"key":"4_CR57","doi-asserted-by":"publisher","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.) European Conference on Computer Vision, vol. 13699, pp. 70\u201389. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19842-7_5","DOI":"10.1007\/978-3-031-19842-7_5"},{"key":"4_CR58","unstructured":"Liu, M., et al.: A survey on autonomous driving datasets: data statistic, annotation, and outlook. arXiv preprint arXiv:2401.01454 (2024)"},{"key":"4_CR59","unstructured":"Liu, Y., et al.: PCSeg: an open source point cloud segmentation codebase (2023). https:\/\/github.com\/PJLab-ADG\/PCSeg"},{"key":"4_CR60","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: UniSeg: a unified multi-modal LiDAR segmentation network and the OpenPCSeg codebase. In: IEEE\/CVF International Conference on Computer Vision, pp. 21662\u201321673 (2023)","DOI":"10.1109\/ICCV51070.2023.01980"},{"key":"4_CR61","unstructured":"Liu, Y., et al.: Segment any point cloud sequences by distilling vision foundation models. In: Advances in Neural Information Processing Systems, vol.\u00a036 (2023)"},{"key":"4_CR62","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Multi-space alignments towards universal lidar segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14648\u201314661 (2024)","DOI":"10.1109\/CVPR52733.2024.01388"},{"key":"4_CR63","unstructured":"Liu, Y.C., et al.: Learning from 2D: contrastive pixel-to-point knowledge transfer for 3D pretraining. arXiv preprint arXiv:2104.04687 (2021)"},{"key":"4_CR64","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, J., Zhang, Z., Huang, J., Yi, L.: LeaF: learning frames for 4D point cloud sequence understanding. In: IEEE\/CVF International Conference on Computer Vision, pp. 604\u2013613 (2023)","DOI":"10.1109\/ICCV51070.2023.00062"},{"key":"4_CR65","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)"},{"key":"4_CR66","doi-asserted-by":"crossref","unstructured":"Mahmoud, A., Hu, J.S., Kuai, T., Harakeh, A., Paull, L., Waslander, S.L.: Self-supervised image-to-point distillation via semantically tolerant contrastive loss. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7102\u20137110 (2023)","DOI":"10.1109\/CVPR52729.2023.00686"},{"key":"4_CR67","doi-asserted-by":"crossref","unstructured":"Michele, B., Boulch, A., Puy, G., Vu, T.H., Marlet, R., Courty, N.: SALUDA: surface-based automotive lidar unsupervised domain adaptation. arXiv preprint arXiv:2304.03251 (2023)","DOI":"10.1109\/3DV62453.2024.00134"},{"key":"4_CR68","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 4213\u20134220 (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"issue":"7","key":"4_CR69","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.1109\/TITS.2020.3032227","volume":"22","author":"K Muhammad","year":"2020","unstructured":"Muhammad, K., Ullah, A., Lloret, J., Ser, J.D., de Albuquerque, V.H.C.: Deep learning for safe autonomous driving: current challenges and future directions. IEEE Trans. Intell. Transp. Syst. 22(7), 4316\u20134336 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"4_CR70","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 Robot. Autom. Lett. 7(2), 2116\u20132123 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"4_CR71","doi-asserted-by":"crossref","unstructured":"Nunes, L., Wiesmann, L., Marcuzzi, R., Chen, X., Behley, J., Stachniss, C.: Temporal consistent 3D LiDAR representation learning for semantic perception in autonomous driving. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5217\u20135228 (2023)","DOI":"10.1109\/CVPR52729.2023.00505"},{"key":"4_CR72","unstructured":"Oquab, M., et al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"4_CR73","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: IEEE Intelligent Vehicles Symposium, pp. 687\u2013693 (2020)","DOI":"10.1109\/IV47402.2020.9304596"},{"key":"4_CR74","doi-asserted-by":"crossref","unstructured":"Pang, B., Xia, H., Lu, C.: Unsupervised 3D point cloud representation learning by triangle constrained contrast for autonomous driving. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5229\u20135239 (2023)","DOI":"10.1109\/CVPR52729.2023.00506"},{"key":"4_CR75","unstructured":"Puy, G., et al.: Revisiting the distillation of image representations into point clouds for autonomous driving. arXiv preprint arXiv:2310.17504 (2023)"},{"key":"4_CR76","doi-asserted-by":"crossref","unstructured":"Puy, G., et al.: Three pillars improving vision foundation model distillation for LiDAR. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21519\u201321529 (2024)","DOI":"10.1109\/CVPR52733.2024.02033"},{"key":"4_CR77","unstructured":"Qiu, H., Yu, B., Tao, D.: GFNet: geometric flow network for 3D point cloud semantic segmentation. Trans. Mach. Learn. Res. (2022)"},{"key":"4_CR78","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"4","key":"4_CR79","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3390\/technologies10040090","volume":"10","author":"G Rizzoli","year":"2022","unstructured":"Rizzoli, G., Barbato, F., Zanuttigh, P.: Multimodal semantic segmentation in autonomous driving: a review of current approaches and future perspectives. Technologies 10(4), 90 (2022)","journal-title":"Technologies"},{"key":"4_CR80","doi-asserted-by":"publisher","unstructured":"Saltori, C., et al.: GIPSO: geometrically informed propagation for online adaptation in 3D LiDAR segmentation. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. LNCS, vol. 13693, pp. 567\u2013585. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19827-4_33","DOI":"10.1007\/978-3-031-19827-4_33"},{"key":"4_CR81","doi-asserted-by":"crossref","unstructured":"Sautier, C., Puy, G., Boulch, A., Marlet, R., Lepetit, V.: BEVContrast: self-supervision in BEV space for automotive LiDAR point clouds. arXiv preprint arXiv:2310.17281 (2023)","DOI":"10.1109\/3DV62453.2024.00017"},{"key":"4_CR82","doi-asserted-by":"crossref","unstructured":"Sautier, C., Puy, G., Gidaris, S., Boulch, A., Bursuc, A., Marlet, R.: Image-to-Lidar self-supervised distillation for autonomous driving data. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9891\u20139901 (2022)","DOI":"10.1109\/CVPR52688.2022.00966"},{"key":"4_CR83","doi-asserted-by":"crossref","unstructured":"Shen, Z., et al.: Masked spatio-temporal structure prediction for self-supervised learning on point cloud videos. In: IEEE\/CVF International Conference on Computer Vision, pp. 16580\u201316589 (2023)","DOI":"10.1109\/ICCV51070.2023.01520"},{"key":"4_CR84","doi-asserted-by":"crossref","unstructured":"Sheng, X., Shen, Z., Xiao, G., Wang, L., Guo, Y., Fan, H.: Point contrastive prediction with semantic clustering for self-supervised learning on point cloud videos. In: IEEE\/CVF International Conference on Computer Vision, pp. 16515\u201316524 (2023)","DOI":"10.1109\/CVPR52729.2023.00123"},{"key":"4_CR85","doi-asserted-by":"crossref","unstructured":"Shi, H., Lin, G., Wang, H., Hung, T.Y., Wang, Z.: SpSequenceNet: semantic segmentation network on 4D point clouds. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4574\u20134583 (2020)","DOI":"10.1109\/CVPR42600.2020.00463"},{"key":"4_CR86","doi-asserted-by":"crossref","unstructured":"Shi, H., Wei, J., Li, R., Liu, F., Lin, G.: Weakly supervised segmentation on outdoor 4D point clouds with temporal matching and spatial graph propagation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11840\u201311849 (2022)","DOI":"10.1109\/CVPR52688.2022.01154"},{"key":"4_CR87","unstructured":"Smith, L.N., Topin, N.: Super-convergence: very fast training of neural networks using large learning rates. arXiv preprint arXiv:1708.07120 (2017)"},{"key":"4_CR88","unstructured":"Sun, J., et al.: An empirical study of training state-of-the-art LiDAR segmentation models. arXiv preprint arXiv:2405.14870 (2024)"},{"key":"4_CR89","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446\u20132454 (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"4_CR90","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-030-58604-1_41","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Tang","year":"2020","unstructured":"Tang, H., et al.: Searching efficient 3D architectures with sparse point-voxel convolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 685\u2013702. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_41"},{"key":"4_CR91","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol.\u00a030 (2017)"},{"key":"4_CR92","doi-asserted-by":"crossref","unstructured":"Triess, L.T., Dreissig, M., Rist, C.B., Z\u00f6llner, J.M.: A survey on deep domain adaptation for LiDAR perception. In: IEEE Intelligent Vehicles Symposium Workshops, pp. 350\u2013357 (2021)","DOI":"10.1109\/IVWorkshops54471.2021.9669228"},{"key":"4_CR93","unstructured":"Uecker, M., Fleck, T., Pflugfelder, M., Z\u00f6llner, J.M.: Analyzing deep learning representations of point clouds for real-time in-vehicle LiDAR perception. arXiv preprint arXiv:2210.14612 (2022)"},{"key":"4_CR94","doi-asserted-by":"crossref","unstructured":"Unal, O., Dai, D., Gool, L.V.: Scribble-supervised LiDAR semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2697\u20132707 (2022)","DOI":"10.1109\/CVPR52688.2022.00272"},{"key":"4_CR95","doi-asserted-by":"crossref","unstructured":"Wei, W., Nejadasl, F.K., Gevers, T., Oswald, M.R.: T-MAE: temporal masked autoencoders for point cloud representation learning. arXiv preprint arXiv:2312.10217 (2023)","DOI":"10.1007\/978-3-031-73247-8_11"},{"key":"4_CR96","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhang, T., Ke, W., S\u00fcsstrunk, S., Salzmann, M.: Spatiotemporal self-supervised learning for point clouds in the wild. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5251\u20135260 (2023)","DOI":"10.1109\/CVPR52729.2023.00508"},{"key":"4_CR97","doi-asserted-by":"crossref","unstructured":"Xiao, A., Huang, J., Guan, D., Zhan, F., Lu, S.: Transfer learning from synthetic to real LiDAR point cloud for semantic segmentation. In: AAAI Conference on Artificial Intelligence, pp. 2795\u20132803 (2022)","DOI":"10.1609\/aaai.v36i3.20183"},{"issue":"9","key":"4_CR98","doi-asserted-by":"publisher","first-page":"11321","DOI":"10.1109\/TPAMI.2023.3262786","volume":"45","author":"A Xiao","year":"2023","unstructured":"Xiao, A., Huang, J., Guan, D., Zhang, X., Lu, S., Shao, L.: Unsupervised point cloud representation learning with deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(9), 11321\u201311339 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR99","doi-asserted-by":"crossref","unstructured":"Xiao, A., et al.: 3D semantic segmentation in the wild: learning generalized models for adverse-condition point clouds. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9382\u20139392 (2023)","DOI":"10.1109\/CVPR52729.2023.00905"},{"key":"4_CR100","unstructured":"Xie, B., Li, S., Guo, Q., Liu, C.H., Cheng, X.: Annotator: a generic active learning baseline for lidar semantic segmentation. In: Advances in Neural Information Processing Systems, vol.\u00a036 (2023)"},{"key":"4_CR101","doi-asserted-by":"publisher","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.) Computer Vision \u2013 ECCV 2020. ECCV 2020. LNCS, vol. 12348, pp. 574\u2013591. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_34","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"4_CR102","unstructured":"Xie, S., et al.: Benchmarking and improving bird\u2019s eye view perception robustness in autonomous driving. arXiv preprint arXiv:2405.17426 (2024)"},{"key":"4_CR103","doi-asserted-by":"crossref","unstructured":"Xie, Z., et al.: SimMIM: a simple framework for masked image modeling. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653\u20139663 (2022)","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"4_CR104","doi-asserted-by":"publisher","unstructured":"Xu, C., et al.: SqueezeSegV3: spatially-adaptive convolution for efficient point-cloud segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision \u2013 ECCV 2020. ECCV 2020. LNCS, vol. 12373, pp. 1\u201319. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_1","DOI":"10.1007\/978-3-030-58604-1_1"},{"key":"4_CR105","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: IEEE\/CVF International Conference on Computer Vision, pp. 16024\u201316033 (2021)","DOI":"10.1109\/ICCV48922.2021.01572"},{"issue":"24","key":"4_CR106","doi-asserted-by":"publisher","first-page":"31461","DOI":"10.1109\/JSEN.2023.3328603","volume":"23","author":"W Xu","year":"2023","unstructured":"Xu, W., Li, X., Ni, P., Guang, X., Luo, H., Zhao, X.: Multi-view fusion driven 3D point cloud semantic segmentation based on hierarchical transformer. IEEE Sens. J. 23(24), 31461\u201331470 (2023)","journal-title":"IEEE Sens. J."},{"key":"4_CR107","unstructured":"Xu, X., Kong, L., Shuai, H., Liu, Q.: FRNet: frustum-range networks for scalable LiDAR segmentation. arXiv preprint arXiv:2312.04484 (2023)"},{"key":"4_CR108","doi-asserted-by":"publisher","unstructured":"Yin, J., et al.: ProposalContrast: unsupervised pre-training for LiDAR-based 3D object detection. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13699, pp. 17\u201333. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19842-7_2","DOI":"10.1007\/978-3-031-19842-7_2"},{"key":"4_CR109","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: A simple framework for open-vocabulary segmentation and detection. In: IEEE\/CVF International Conference on Computer Vision, pp. 1020\u20131031 (2023)","DOI":"10.1109\/ICCV51070.2023.00100"},{"key":"4_CR110","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-023-01981-w","volume":"132","author":"S Zhang","year":"2024","unstructured":"Zhang, S., Deng, J., Bai, L., Li, H., Ouyang, W., Zhang, Y.: HVDistill: transferring knowledge from images to point clouds via unsupervised hybrid-view distillation. Int. J. Comput. Vision 132, 1\u201315 (2024)","journal-title":"Int. J. Comput. Vision"},{"key":"4_CR111","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: PolarNet: an improved grid representation for online LiDAR point clouds semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9601\u20139610 (2020)","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"4_CR112","first-page":"1","volume":"132","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Hou, J., Yuan, Y.: A comprehensive study of the robustness for LiDAR-based 3D object detectors against adversarial attacks. Int. J. Comput. Vision 132, 1\u201333 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"4_CR113","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Girdhar, R., Joulin, A., Misra, I.: Self-supervised pretraining of 3D features on any point-cloud. In: IEEE\/CVF International Conference on Computer Vision, pp. 10252\u201310263 (2021)","DOI":"10.1109\/ICCV48922.2021.01009"},{"key":"4_CR114","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Dong, Y., Liu, Y., Yi, L.: Complete-to-partial 4D distillation for self-supervised point cloud sequence representation learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17661\u201317670 (2023)","DOI":"10.1109\/CVPR52729.2023.01694"},{"key":"4_CR115","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, B., Wang, B., Li, B.: GrowSP: unsupervised semantic segmentation of 3D point clouds. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17619\u201317629 (2023)","DOI":"10.1109\/CVPR52729.2023.01690"},{"key":"4_CR116","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Bai, L., Huang, X.: FIDNet: LiDAR point cloud semantic segmentation with fully interpolation decoding. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 4453\u20134458 (2021)","DOI":"10.1109\/IROS51168.2021.9636385"},{"key":"4_CR117","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, Y., Foroosh, H.: Panoptic-PolarNet: proposal-free LiDAR point cloud panoptic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13194\u201313203 (2021)","DOI":"10.1109\/CVPR46437.2021.01299"},{"key":"4_CR118","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9939\u20139948 (2021)","DOI":"10.1109\/CVPR46437.2021.00981"},{"key":"4_CR119","doi-asserted-by":"crossref","unstructured":"Zou, X., et al.: Generalized decoding for pixel, image, and language. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15116\u201315127 (2023)","DOI":"10.1109\/CVPR52729.2023.01451"},{"key":"4_CR120","unstructured":"Zou, X., et al.: Segment everything everywhere all at once. In: Advances in Neural Information Processing Systems, vol.\u00a036 (2023)"}],"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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T21:14:59Z","timestamp":1732828499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73232-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031732317","9783031732324"],"references-count":120,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73232-4_4","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"}}]}}