{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:26:15Z","timestamp":1780392375742,"version":"3.54.1"},"publisher-location":"Cham","reference-count":98,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729324","type":"print"},{"value":"9783031729331","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"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\/978-3-031-72933-1_5","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"71-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Better Call SAL: Towards Learning to\u00a0Segment Anything in\u00a0Lidar"],"prefix":"10.1007","author":[{"given":"Aljo\u0161a","family":"O\u0161ep","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Meinhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Ferroni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neehar","family":"Peri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deva","family":"Ramanan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Leal-Taix\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"issue":"11","key":"5_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":"5_CR2","doi-asserted-by":"crossref","unstructured":"Agarwalla, A., et al.: Lidar panoptic segmentation and tracking without bells and whistles. In: International Conference on Intelligent Robots and Systems (2023)","DOI":"10.1109\/IROS55552.2023.10341415"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Aksoy, E.E., Baci, S., Cavdar, S.: SalsaNet: fast road and vehicle segmentation in lidar point clouds for autonomous driving. In: Intelligent Vehicles Symposium (2020)","DOI":"10.1109\/IV47402.2020.9304694"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Ayg\u00fcn, M., et al.: 4D panoptic lidar segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00548"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-shot object detection. In: European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-01246-5_24"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Behley, J., Milioto, A., Stachniss, C.: A benchmark for LiDAR-based panoptic segmentation based on KITTI. In: International Conference on Robotics and Automation (2021)","DOI":"10.1109\/ICRA48506.2021.9561476"},{"key":"5_CR8","unstructured":"Bucher, M., Vu, T.H., Cord, M., P\u00e9rez, P.: Zero-shot semantic segmentation. In: Advances in Neural Information Processing Systems (2019)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"5_CR10","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems (2020)"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5_CR16","unstructured":"Ding, Z., Wang, J., Tu, Z.: Open-vocabulary universal image segmentation with maskclip. In: International Conference on Machine Learning (2023)"},{"key":"5_CR17","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: Robotics: Science and Systems (1996)"},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"3795","DOI":"10.1109\/LRA.2022.3148457","volume":"7","author":"WK Fong","year":"2021","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 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1109\/LRA.2021.3060405","volume":"6","author":"S Gasperini","year":"2021","unstructured":"Gasperini, S., Mahani, M.A.N., Marcos-Ramiro, A., Navab, N., Tombari, F.: Panoster: end-to-end panoptic segmentation of lidar point clouds. IEEE Robot. Autom. Lett. 6, 3216\u20133223 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Gu, X., Cui, Y., Lin, T.Y.: Scaling open-vocabulary image segmentation with image-level labels. In: European Conference on Computer Vision (2022)","DOI":"10.1007\/978-3-031-20059-5_31"},{"key":"5_CR21","unstructured":"Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921 (2021)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Harley, A.W., et al.: Track, check, repeat: an EM approach to unsupervised tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01631"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"5_CR24","unstructured":"Held, D., Guillory, D., Rebsamen, B., Thrun, S., Savarese, S.: A probabilistic framework for real-time 3D segmentation using spatial, temporal, and semantic cues. In: Robotics: Science and Systems (2016)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Held, D., Levinson, J., Thrun, S., Savarese, S.: Combining 3D shape, color, and motion for robust anytime tracking. In: Robotics: Science and Systems (2014)","DOI":"10.15607\/RSS.2014.X.014"},{"key":"5_CR26","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 Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01289"},{"issue":"2","key":"5_CR27","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1109\/LRA.2020.2965389","volume":"5","author":"P Hu","year":"2020","unstructured":"Hu, P., Held, D., Ramanan, D.: Learning to optimally segment point clouds. IEEE Robot. Autom. Lett. 5(2), 875\u2013882 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"5_CR28","unstructured":"Hurtado, J.V., Mohan, R., Valada, A.: MOPT: multi-object panoptic tracking. arXiv preprint arXiv:2004.08189 (2020)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R.B., Rother, C., Doll\u00e1r, P.: Panoptic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2019.00963"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Kreuzberg, L., Zulfikar, I.E., Mahadevan, S., Engelmann, F., Leibe, B.: 4D-stop: panoptic segmentation of 4D lidar using spatio-temporal object proposal generation and aggregation. In: ECCV AVVision Workshop (2022)","DOI":"10.1007\/978-3-031-25056-9_34"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"5_CR33","unstructured":"Li, B., Weinberger, K.Q., Belongie, S., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. In: International Conference on Learning Representations (2022)"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Li, J., He, X., Wen, Y., Gao, Y., Cheng, Y., Zhang, D.: Panoptic-PHNet: towards real-time and high-precision lidar panoptic segmentation via clustering pseudo heatmap. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01151"},{"issue":"2","key":"5_CR35","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1109\/LRA.2021.3132059","volume":"7","author":"S Li","year":"2021","unstructured":"Li, S., Chen, X., Liu, Y., Dai, D., Stachniss, C., Gall, J.: Multi-scale interaction for real-time lidar data segmentation on an embedded platform. IEEE Robot. Autom. Lett. 7(2), 738\u2013745 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Liang, F., et al.: Open-vocabulary semantic segmentation with mask-adapted clip. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00682"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Lin, T., et al.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"5_CR38","unstructured":"Lin, Z., Pathak, D., Wang, Y.X., Ramanan, D., Kong, S.: Continual learning with evolving class ontologies. In: Advances in Neural Information Processing Systems (2022)"},{"key":"5_CR39","unstructured":"Liu, Y., et al.: Segment any point cloud sequences by distilling vision foundation models. arXiv preprint arXiv:2306.09347 (2023)"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, Z., Cao, Y., Hu, H., Tong, X.: Group-free 3D object detection via transformers. In: International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00294"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Lu, Y., Jiang, Q., Chen, R., Hou, Y., Zhu, X., Ma, Y.: See more and know more: zero-shot point cloud segmentation via multi-modal visual data. In: International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.01981"},{"key":"5_CR42","unstructured":"Ma, Y., et al.: Long-tailed 3D detection via 2D late fusion. arXiv preprint arXiv:2312.10986 (2023)"},{"issue":"2","key":"5_CR43","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1109\/LRA.2023.3236568","volume":"8","author":"R Marcuzzi","year":"2023","unstructured":"Marcuzzi, R., Nunes, L., Wiesmann, L., Behley, J., Stachniss, C.: Mask-based panoptic lidar segmentation for autonomous driving. IEEE Robot. Autom. Lett. 8(2), 1141\u20131148 (2023)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Marcuzzi, R., Nunes, L., Wiesmann, L., Marks, E., Behley, J., Stachniss, C.: Mask4D: end-to-end mask-based 4D panoptic segmentation for lidar sequences. IEEE Robot. Autom. Lett. (2023)","DOI":"10.1109\/LRA.2023.3320020"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"Marcuzzi, R., Nunes, L., Wiesmann, L., Vizzo, I., Behley, J., Stachniss, C.: Contrastive instance association for 4D panoptic segmentation using sequences of 3D lidar scans. IEEE Robot. Autom. Lett. (2022)","DOI":"10.1109\/LRA.2022.3140439"},{"key":"5_CR46","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: International Conference on Intelligent Robots and Systems (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"5_CR48","doi-asserted-by":"crossref","unstructured":"Miller, D., Nicholson, L., Dayoub, F., S\u00fcnderhauf, N.: Dropout sampling for robust object detection in open-set conditions. In: International Conference on Robotics and Automation (2018)","DOI":"10.1109\/ICRA.2018.8460700"},{"key":"5_CR49","doi-asserted-by":"crossref","unstructured":"Moosmann, F., Stiller, C.: Joint self-localization and tracking of generic objects in 3D range data. In: International Conference on Robotics and Automation (2013)","DOI":"10.1109\/ICRA.2013.6630716"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Najibi, M., et al.: Motion inspired unsupervised perception and prediction in autonomous driving. In: European Conference on Computer Vision (2022)","DOI":"10.1007\/978-3-031-19839-7_25"},{"key":"5_CR51","doi-asserted-by":"crossref","unstructured":"Najibi, M., et al.: Unsupervised 3D perception with 2D vision-language distillation for autonomous driving. In: International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.00790"},{"issue":"2","key":"5_CR52","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":"5_CR53","unstructured":"Osep, A., Voigtlaender, P., Luiten, J., Breuers, S., Leibe, B.: Towards large-scale video video object mining. In: ECCV Workshop on Interactive and Adaptive Learning in an Open World (2018)"},{"key":"5_CR54","doi-asserted-by":"crossref","unstructured":"O\u0161ep, A., Mehner, W., Voigtlaender, P., Leibe, B.: Track, then decide: category-agnostic vision-based multi-object tracking. In: International Conference on Robotics and Automation (2018)","DOI":"10.1109\/ICRA.2018.8460975"},{"key":"5_CR55","doi-asserted-by":"crossref","unstructured":"O\u0161ep, A., Voigtlaender, P., Luiten, J., Breuers, S., Leibe, B.: Large-scale object mining for object discovery from unlabeled video. In: International Conference on Robotics and Automation (2019)","DOI":"10.1109\/ICRA.2019.8793683"},{"key":"5_CR56","doi-asserted-by":"crossref","unstructured":"Peng, S., Genova, K., Jiang, C., Tagliasacchi, A., Pollefeys, M., Funkhouser, T.: OpenScene: 3D scene understanding with open vocabularies. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00085"},{"key":"5_CR57","unstructured":"Peri, N., Dave, A., Ramanan, D., Kong, S.: Towards long-tailed 3D detection. In: Conference on Robot Learning (2023)"},{"key":"5_CR58","doi-asserted-by":"crossref","unstructured":"Peri, N., Li, M., Wilson, B., Wang, Y.X., Hays, J., Ramanan, D.: An empirical analysis of range for 3D object detection. In: ICCV Workshops (2023)","DOI":"10.1109\/ICCVW60793.2023.00440"},{"key":"5_CR59","doi-asserted-by":"crossref","unstructured":"Peri, N., Luiten, J., Li, M., O\u0161ep, A., Leal-Taix\u00e9, L., Ramanan, D.: Forecasting from lidar via future object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01669"},{"key":"5_CR60","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s10514-009-9115-1","volume":"26","author":"A Petrovskaya","year":"2009","unstructured":"Petrovskaya, A., Thrun, S.: Model based vehicle detection and tracking for autonomous urban driving. Auton. Rob. 26, 123\u2013139 (2009)","journal-title":"Auton. Rob."},{"key":"5_CR61","unstructured":"Pot, E., Toshev, A., Kosecka, J.: Self-supervisory signals for object discovery and detection. arXiv preprint arXiv:1806.03370 (2018)"},{"key":"5_CR62","doi-asserted-by":"crossref","unstructured":"Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)","DOI":"10.1109\/CVPR.2012.6248065"},{"key":"5_CR63","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)"},{"key":"5_CR64","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (2017)"},{"key":"5_CR65","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning (2021)"},{"key":"5_CR66","doi-asserted-by":"crossref","unstructured":"Rahman, S., Khan, S.H., Porikli, F.: Zero-shot object detection: learning to simultaneously recognize and localize novel concepts. In: Asian Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-20887-5_34"},{"key":"5_CR67","doi-asserted-by":"crossref","unstructured":"Rao, Y., et al.: DenseCLIP: language-guided dense prediction with context-aware prompting. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01755"},{"key":"5_CR68","doi-asserted-by":"crossref","unstructured":"Razani, R., Cheng, R., Li, E., Taghavi, E., Ren, Y., Bingbing, L.: GP-S3Net: graph-based panoptic sparse semantic segmentation network. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/ICCV48922.2021.01577"},{"key":"5_CR69","doi-asserted-by":"crossref","unstructured":"Razani, R., Cheng, R., Taghavi, E., Bingbing, L.: Lite-HDSeg: lidar semantic segmentation using lite harmonic dense convolutions. In: International Conference on Robotics and Automation (2021)","DOI":"10.1109\/ICRA48506.2021.9561171"},{"key":"5_CR70","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 Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00966"},{"key":"5_CR71","doi-asserted-by":"crossref","unstructured":"Seidenschwarz, J., O\u0161ep, A., Ferroni, F., Lucey, S., Leal-Taix\u00e9, L.: SeMoLi: what moves together belongs together. In: IEEE Conference on Computer Vision and Pattern Recognition (2024)","DOI":"10.1109\/CVPR52733.2024.01391"},{"key":"5_CR72","doi-asserted-by":"crossref","unstructured":"Sirohi, K., Mohan, R., B\u00fcscher, D., Burgard, W., Valada, A.: EfficientLPS: efficient lidar panoptic segmentation. IEEE Trans. Robot. (2021)","DOI":"10.1109\/TRO.2021.3122069"},{"key":"5_CR73","doi-asserted-by":"crossref","unstructured":"Sun, P., et\u00a0al.: Scalability in perception for autonomous driving: waymo open dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"5_CR74","unstructured":"Takmaz, A., Fedele, E., Sumner, R.W., Pollefeys, M., Tombari, F., Engelmann, F.: Openmask3D: open-vocabulary 3D instance segmentation. arXiv preprint arXiv:2306.13631 (2023)"},{"key":"5_CR75","doi-asserted-by":"crossref","unstructured":"Tang, H., et al.: Searching efficient 3D architectures with sparse point-voxel convolution. In: European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58604-1_41"},{"key":"5_CR76","doi-asserted-by":"crossref","unstructured":"Teichman, A., Levinson, J., Thrun, S.: Towards 3D object recognition via classification of arbitrary object tracks. In: International Conference on Robotics and Automation (2011)","DOI":"10.1109\/ICRA.2011.5979636"},{"key":"5_CR77","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"issue":"4","key":"5_CR78","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/64.85919","volume":"6","author":"C Thorpe","year":"1991","unstructured":"Thorpe, C., Herbert, M., Kanade, T., Shafer, S.: Toward autonomous driving: the CMU Navlab. I. perception. IEEE Expert 6(4), 31\u201342 (1991)","journal-title":"IEEE Expert"},{"key":"5_CR79","doi-asserted-by":"crossref","unstructured":"Thrun, S., et al.: Stanley: the robot that won the DARPA grand challenge. J. Field Robot. (2006)","DOI":"10.1007\/11871842_4"},{"key":"5_CR80","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Train in Germany, test in the USA: making 3D object detectors generalize. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01173"},{"key":"5_CR81","unstructured":"Wong, K., Wang, S., Ren, M., Liang, M., Urtasun, R.: Identifying unknown instances for autonomous driving. In: Conference on Robot Learning, pp. 384\u2013393. PMLR (2020)"},{"key":"5_CR82","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: International Conference on Robotics and Automation (2018)","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"5_CR83","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: SqueezeSegV2: improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In: International Conference on Robotics and Automation (2019)","DOI":"10.1109\/ICRA.2019.8793495"},{"key":"5_CR84","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2018","unstructured":"Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41, 2251\u20132265 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR85","doi-asserted-by":"crossref","unstructured":"Xiong, X., Munoz, D., Bagnell, J.A., Hebert, M.: 3-D scene analysis via sequenced predictions over points and regions. In: International Conference on Robotics and Automation, pp. 2609\u20132616 (2011)","DOI":"10.1109\/ICRA.2011.5980125"},{"key":"5_CR86","doi-asserted-by":"crossref","unstructured":"Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De\u00a0Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00289"},{"key":"5_CR87","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X.: Side adapter network for open-vocabulary semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00288"},{"issue":"10","key":"5_CR88","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.3390\/s18103337","volume":"18","author":"Y Yan","year":"2018","unstructured":"Yan, Y., Mao, Y., Li, B.: Second: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)","journal-title":"Sensors"},{"key":"5_CR89","doi-asserted-by":"crossref","unstructured":"Yilmaz, K., Schult, J., Nekrasov, A., Leibe, B.: Mask4D: mask transformer for 4D panoptic segmentation. arXiv preprint arXiv:2309.16133 (2023)","DOI":"10.1109\/ICRA57147.2024.10610262"},{"key":"5_CR90","doi-asserted-by":"crossref","unstructured":"Yin, T., Zhou, X., Kr\u00e4henb\u00fchl, P.: Center-based 3D object detection and tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"5_CR91","doi-asserted-by":"crossref","unstructured":"Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01416"},{"key":"5_CR92","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: Towards unsupervised object detection from lidar point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00899"},{"key":"5_CR93","doi-asserted-by":"crossref","unstructured":"Zhong, Y., et\u00a0al.: RegionCLIP: region-based language-image pretraining. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01629"},{"key":"5_CR94","doi-asserted-by":"crossref","unstructured":"Zhou, C., Loy, C.C., Dai, B.: Extract free dense labels from clip. In: European Conference on Computer Vision (2022)","DOI":"10.1007\/978-3-031-19815-1_40"},{"key":"5_CR95","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00472"},{"key":"5_CR96","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, Y., Foroosh, H.: Panoptic-polarnet: proposal-free lidar point cloud panoptic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01299"},{"key":"5_CR97","doi-asserted-by":"crossref","unstructured":"Zhu, M., Han, S., Cai, H., Borse, S., Ghaffari, M., Porikli, F.: 4D panoptic segmentation as invariant and equivariant field prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/ICCV51070.2023.02055"},{"key":"5_CR98","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for lidar segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)","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\/978-3-031-72933-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:32:54Z","timestamp":1727872374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72933-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,3]]},"ISBN":["9783031729324","9783031729331"],"references-count":98,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72933-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,3]]},"assertion":[{"value":"3 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"}}]}}