{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:33:36Z","timestamp":1769632416016,"version":"3.49.0"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11263-024-02166-9","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T06:03:00Z","timestamp":1727157780000},"page":"1153-1174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Lidar Panoptic Segmentation in an Open World"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8400-6762","authenticated-orcid":false,"given":"Anirudh S.","family":"Chakravarthy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meghana Reddy","family":"Ganesina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiyun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Leal-Taix\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deva","family":"Ramanan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aljosa","family":"Osep","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"issue":"11","key":"2166_CR1","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1109\/TPAMI.2012.28","volume":"34","author":"B Alexe","year":"2012","unstructured":"Alexe, B., Deselaers, T., & Ferrari, V. (2012). Measuring the objectness of image windows. IEEE TPAMI, 34(11), 2189\u20132202.","journal-title":"IEEE TPAMI"},{"issue":"4","key":"2166_CR2","doi-asserted-by":"publisher","first-page":"5432","DOI":"10.1109\/LRA.2020.3007440","volume":"5","author":"I Alonso","year":"2020","unstructured":"Alonso, I., Riazuelo, L., Montesano, L., & Murillo, A. C. (2020). 3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation. IEEE Robotics and Automation Letters, 5(4), 5432\u20135439.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2166_CR3","doi-asserted-by":"crossref","unstructured":"Ayg\u00fcn, M., Osep, A., Weber, M., Maximov, M., Stachniss, C., Behley, J., & Leal-Taix\u00e9, L. (2021). 4d panoptic lidar segmentation. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00548"},{"key":"2166_CR4","doi-asserted-by":"crossref","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019). SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. In ICCV.","DOI":"10.1109\/ICCV.2019.00939"},{"key":"2166_CR5","doi-asserted-by":"crossref","unstructured":"Behley, J., Milioto, A., & Stachniss, C. (2021). A benchmark for LiDAR-based panoptic segmentation based on KITTI. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA48506.2021.9561476"},{"key":"2166_CR6","doi-asserted-by":"crossref","unstructured":"Behley, J., Steinhage, V., & Cremers, A.B. (2013). Laser-based segment classification using a mixture of bag-of-words. In International Conference on Intelligent Robots and Systems.","DOI":"10.1109\/IROS.2013.6696957"},{"key":"2166_CR7","doi-asserted-by":"crossref","unstructured":"Bendale, A., & Boult, T.E. (2016) Towards open set deep networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563\u20131572.","DOI":"10.1109\/CVPR.2016.173"},{"key":"2166_CR8","doi-asserted-by":"crossref","unstructured":"Boykov, Y., & Funka-Lea, G. (2006). Graph cuts and efficient nd image segmentation. In IJCV70(2).","DOI":"10.1007\/s11263-006-7934-5"},{"key":"2166_CR9","doi-asserted-by":"crossref","unstructured":"Cen, J., Yun, P., Zhang, S., Cai, J., Luan, D., Tang, M., Liu, M., & Yu\u00a0Wang, M. (2022). Open-world semantic segmentation for lidar point clouds. In ECCV.","DOI":"10.1007\/978-3-031-19839-7_19"},{"key":"2166_CR10","unstructured":"Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., & Urtasun, R. (2015). 3D object proposals for accurate object class detection. In Advances in Neural Information Processing Systems."},{"key":"2166_CR11","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., & Savarese, S. (2019). 4D spatio-temporal convnets: Minkowski convolutional neural networks. In CVPR.","DOI":"10.1109\/CVPR.2019.00319"},{"key":"2166_CR12","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In CVPR.","DOI":"10.1109\/CVPR.2016.350"},{"key":"2166_CR13","doi-asserted-by":"crossref","unstructured":"Dewan, A., Caselitz, T., Tipaldi, G.D., & Burgard, W. (2015). Motion-based detection and tracking in 3d lidar scans. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA.2016.7487649"},{"key":"2166_CR14","unstructured":"Dhamija, A.R., G\u00fcnther, M., & Boult, T.E. (2018). Reducing network agnostophobia. In NeurIPS."},{"key":"2166_CR15","doi-asserted-by":"crossref","unstructured":"Dhamija, A., Gunther, M., Ventura, J., & Boult, T. (2020). The overlooked elephant of object detection: Open set. In Wint. Conf. App. Comput. Vis.","DOI":"10.1109\/WACV45572.2020.9093355"},{"key":"2166_CR16","doi-asserted-by":"crossref","unstructured":"Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., & Frenkel, A. (2011). On the segmentation of 3d lidar point clouds. In 2011 IEEE International Conference on Robotics and Automation, pp. 2798\u20132805. IEEE.","DOI":"10.1109\/ICRA.2011.5979818"},{"key":"2166_CR17","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Robotics: Science and Systems."},{"issue":"2","key":"2166_CR18","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (VOC) challenge. IJCV, 88(2), 303\u2013338.","journal-title":"IJCV"},{"key":"2166_CR19","unstructured":"Fomenko, V., Elezi, I., Ramanan, D., Leal-Taix\u2019e, L., & Osep, A. (2022). Learning to discover and detect objects. In Advances in Neural Information Processing Systems."},{"key":"2166_CR20","doi-asserted-by":"crossref","unstructured":"Fong, W.K., Mohan, R., Hurtado, J.V., Zhou, L., Caesar, H., Beijbom, O., & Valada, A. (2021). Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. arXiv preprint arXiv:2109.03805","DOI":"10.1109\/LRA.2022.3148457"},{"key":"2166_CR21","doi-asserted-by":"crossref","unstructured":"Gasperini, S., Mahani, M.-A.N., Marcos-Ramiro, A., Navab, N., & Tombari, F. (2021). Panoster: End-to-end panoptic segmentation of lidar point clouds. Letters: IEEE Rob. Automat.","DOI":"10.1109\/LRA.2021.3060405"},{"key":"2166_CR22","unstructured":"Held, D., Guillory, D., Rebsamen, B., Thrun, S., & Savarese, S. (2016). A probabilistic framework for real-time 3d segmentation using spatial, temporal, and semantic cues. In Robotics: Science and Systems."},{"key":"2166_CR23","unstructured":"Hendrycks, D., Mazeika, M., & Dietterich, T. (2019). Deep anomaly detection with outlier exposure. In ICLR."},{"key":"2166_CR24","doi-asserted-by":"crossref","unstructured":"Hong, F., Zhou, H., Zhu, X., Li, H., & Liu, Z. (2021). Lidar-based panoptic segmentation via dynamic shifting network. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01289"},{"key":"2166_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3349304","author":"F Hong","year":"2024","unstructured":"Hong, F., Kong, L., Zhou, H., Zhu, X., Li, H., & Liu, Z. (2024). Unified 3d and 4d panoptic segmentation via dynamic shifting networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2023.3349304","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"2166_CR26","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. (2020). Learning to optimally segment point clouds. IEEE Robotics and Automation Letters, 5(2), 875\u2013882.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2166_CR27","doi-asserted-by":"crossref","unstructured":"Hwang, J., Oh, S.W., Lee, J.-Y., & Han, B. (2021). Exemplar-based open-set panoptic segmentation network. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00123"},{"key":"2166_CR28","doi-asserted-by":"crossref","unstructured":"Jiang, P., & Saripalli, S. (2021). Lidarnet: A boundary-aware domain adaptation model for point cloud semantic segmentation. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 2457\u20132464. IEEE.","DOI":"10.1109\/ICRA48506.2021.9561255"},{"key":"2166_CR29","doi-asserted-by":"crossref","unstructured":"Joseph, K.J., Khan, S., Khan, F.S., & Balasubramanian, V.N. (2021). Towards open world object detection. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00577"},{"key":"2166_CR30","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., & Doll\u00e1r, P. (2019). Panoptic segmentation. In CVPR.","DOI":"10.1109\/CVPR.2019.00963"},{"key":"2166_CR31","doi-asserted-by":"crossref","unstructured":"Klasing, K., Wollherr, D., & Buss, M. (2008). A clustering method for efficient segmentation of 3d laser data. In 2008 IEEE International Conference on Robotics and Automation, pp. 4043\u20134048. IEEE.","DOI":"10.1109\/ROBOT.2008.4543832"},{"key":"2166_CR32","doi-asserted-by":"crossref","unstructured":"Kong, S., & Fowlkes, C.C. (2018). Recurrent pixel embedding for instance grouping. In CVPR.","DOI":"10.1109\/CVPR.2018.00940"},{"key":"2166_CR33","doi-asserted-by":"crossref","unstructured":"Kong, S., & Ramanan, D. (2021). Opengan: Open-set recognition via open data generation. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00085"},{"key":"2166_CR34","doi-asserted-by":"crossref","unstructured":"Kong, L., Quader, N., & Liong, V.E. (2023). Conda: Unsupervised domain adaptation for lidar segmentation via regularized domain concatenation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9338\u20139345. IEEE.","DOI":"10.1109\/ICRA48891.2023.10160410"},{"key":"2166_CR35","doi-asserted-by":"crossref","unstructured":"Kreuzberg, L., Zulfikar, I.E., Mahadevan, S., Engelmann, F., & Leibe, B. (2022). 4d-stop: Panoptic segmentation of 4d lidar using spatio-temporal object proposal generation and aggregation. In ECCV AVVision Workshop.","DOI":"10.1007\/978-3-031-25056-9_34"},{"key":"2166_CR36","doi-asserted-by":"crossref","unstructured":"Langer, F., Milioto, A., Haag, A., Behley, J., & Stachniss, C. (2020). Domain transfer for semantic segmentation of lidar data using deep neural networks. In 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8263\u20138270. IEEE","DOI":"10.1109\/IROS45743.2020.9341508"},{"key":"2166_CR37","doi-asserted-by":"crossref","unstructured":"Li, J., He, X., Wen, Y., Gao, Y., Cheng, X., & Zhang, D. (2022). Panoptic-phnet: Towards real-time and high-precision lidar panoptic segmentation via clustering pseudo heatmap. In CVPR.","DOI":"10.1109\/CVPR52688.2022.01151"},{"key":"2166_CR38","doi-asserted-by":"crossref","unstructured":"Li, E., Razani, R., Xu, Y., & Liu, B. (2023). Cpseg: Cluster-free panoptic segmentation of 3d lidar point clouds. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 8239\u20138245. IEEE.","DOI":"10.1109\/ICRA48891.2023.10160705"},{"key":"2166_CR39","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, G., Pan, H., & Wang, Z. (2022). Cpgnet: Cascade point-grid fusion network for real-time lidar semantic segmentation. In 2022 International Conference on Robotics and Automation (ICRA), pp. 11117\u201311123. IEEE.","DOI":"10.1109\/ICRA46639.2022.9811767"},{"key":"2166_CR40","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, G., Wang, B., Hu, Y., & Yin, B. (2023). Center focusing network for real-time lidar panoptic segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13425\u201313434.","DOI":"10.1109\/CVPR52729.2023.01290"},{"key":"2166_CR41","unstructured":"Liao, Y., Xie, J., & Geiger, A. (2021). KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. arXiv preprint arXiv:2109.13410"},{"key":"2166_CR42","first-page":"7671","volume":"35","author":"Z Lin","year":"2022","unstructured":"Lin, Z., Pathak, D., Wang, Y.-X., Ramanan, D., & Kong, S. (2022). Continual learning with evolving class ontologies. Advances in Neural Information Processing Systems, 35, 7671\u20137684.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2166_CR43","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zulfikar, I.E., Luiten, J., Dave, A., Ramanan, D., Leibe, B., Osep, A., & Leal-Taix\u00e9, L. (2022). Opening up open world tracking. In CVPR.","DOI":"10.1109\/CVPR52688.2022.01846"},{"key":"2166_CR44","doi-asserted-by":"crossref","unstructured":"Loiseau, R., Aubry, M., & Landrieu, L. (2022). Online segmentation of lidar sequences: Dataset and algorithm. In European Conference on Computer Vision, pp. 301\u2013317. Springer.","DOI":"10.1007\/978-3-031-19839-7_18"},{"issue":"2","key":"2166_CR45","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. (2023). Mask-based panoptic lidar segmentation for autonomous driving. IEEE Robotics and Automation Letters, 8(2), 1141\u20131148.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2166_CR46","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV.","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"11","key":"2166_CR47","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes, L., Healy, J., & Astels, S. (2017). HDBSCAN: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","journal-title":"Journal of Open Source Software"},{"key":"2166_CR48","doi-asserted-by":"crossref","unstructured":"Moosmann, F., & Stiller, C. (2013). Joint self-localization and tracking of generic objects in 3d range data. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA.2013.6630716"},{"key":"2166_CR49","doi-asserted-by":"crossref","unstructured":"Moosmann, F., Pink, O., & Stiller, C. (2009). Segmentation of 3d lidar data in non-flat urban environments using a local convexity criterion. In IEEE Intelligent Vehicles Symposium.","DOI":"10.1109\/IVS.2009.5164280"},{"key":"2166_CR50","unstructured":"Mosig, C. (2022). ROS package to publish the KITTI-360 dataset. https:\/\/github.com\/dcmlr\/kitti360_ros_player"},{"key":"2166_CR51","doi-asserted-by":"crossref","unstructured":"Najibi, M., Ji, J., Zhou, Y., Qi, C.R., Yan, X., Ettinger, S., & Anguelov, D. (2022). Motion inspired unsupervised perception and prediction in autonomous driving. In ECCV.","DOI":"10.1007\/978-3-031-19839-7_25"},{"key":"2166_CR52","doi-asserted-by":"crossref","unstructured":"Najibi, M., Ji, J., Zhou, Y., Qi, C.R., Yan, X., Ettinger, S., & Anguelov, D. (2023). Unsupervised 3d perception with 2d vision-language distillation for autonomous driving. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 8602\u20138612","DOI":"10.1109\/ICCV51070.2023.00790"},{"key":"2166_CR53","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota\u00a0Bulo, S., & Kontschieder, P. (2017). The mapillary vistas dataset for semantic understanding of street scenes. In Proceedings of the IEEE International Conference on Computer Vision, pp. 4990\u20134999.","DOI":"10.1109\/ICCV.2017.534"},{"issue":"4","key":"2166_CR54","doi-asserted-by":"publisher","first-page":"8713","DOI":"10.1109\/LRA.2022.3187872","volume":"7","author":"L Nunes","year":"2022","unstructured":"Nunes, L., Chen, X., Marcuzzi, R., Osep, A., Leal-Taix\u00e9, L., Stachniss, C., & Behley, J. (2022). Unsupervised class-agnostic instance segmentation of 3d lidar data for autonomous vehicles. IEEE Robotics and Automation Letters, 7(4), 8713\u20138720.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2166_CR55","doi-asserted-by":"crossref","unstructured":"Osep, A., Mehner, W., Voigtlaender, P., & Leibe, B. (2018). Track, then decide: Category-agnostic vision-based multi-object tracking. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA.2018.8460975"},{"key":"2166_CR56","unstructured":"Osep, A., Voigtlaender, P., Luiten, J., Breuers, S., & Leibe, B. (2018). Towards large-scale video video object mining. In ECCV Workshop on Interactive and Adaptive Learning in an Open World."},{"key":"2166_CR57","doi-asserted-by":"crossref","unstructured":"Osep, A., Voigtlaender, P., Luiten, J., Breuers, S., & Leibe, B. (2019). Large-scale object mining for object discovery from unlabeled video. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA.2019.8793683"},{"key":"2166_CR58","doi-asserted-by":"crossref","unstructured":"Osep, A., Voigtlaender, P., Weber, M., Luiten, J., & Leibe, B. (2020). 4d generic video object proposals. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA40945.2020.9196949"},{"key":"2166_CR59","doi-asserted-by":"crossref","unstructured":"Oza, P., & Patel, V.M. (2019). C2AE: Class conditioned auto-encoder for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR.2019.00241"},{"key":"2166_CR60","unstructured":"Qi, C.R., Su, H., Mo, K., & Guibas, L.J. (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR."},{"key":"2166_CR61","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., & Guibas, L.J. (2016). Volumetric and multi-view cnns for object classification on 3d data. In CVPR.","DOI":"10.1109\/CVPR.2016.609"},{"key":"2166_CR62","unstructured":"Qi, C.R., Yi, L., Su, H., & Guibas, L.J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In NeurIPS."},{"key":"2166_CR63","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J. (2021). Learning transferable visual models from natural language supervision."},{"key":"2166_CR64","doi-asserted-by":"crossref","unstructured":"Razani, R., Cheng, R., Li, E., Taghavi, E., Ren, Y., & Bingbing, L. (2021). Gp-s3net: Graph-based panoptic sparse semantic segmentation network. In CVPR.","DOI":"10.1109\/ICCV48922.2021.01577"},{"key":"2166_CR65","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In NeurIPS."},{"issue":"9","key":"2166_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3472291","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2021). A survey of deep active learning. ACM Computing Surveys (CSUR), 54(9), 1\u201340.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"2166_CR67","doi-asserted-by":"crossref","unstructured":"Rist, C.B., Enzweiler, M., & Gavrila, D.M. (2019). Cross-sensor deep domain adaptation for lidar detection and segmentation. In 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1535\u20131542. IEEE","DOI":"10.1109\/IVS.2019.8814047"},{"issue":"7","key":"2166_CR68","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2012","unstructured":"Scheirer, W. J., Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2012). Toward open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1757\u20131772.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2166_CR69","doi-asserted-by":"crossref","unstructured":"Shaban, A., Lee, J., Jung, S., Meng, X., & Boots, B. (2023). Lidar-uda: Self-ensembling through time for unsupervised lidar domain adaptation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19784\u201319794.","DOI":"10.1109\/ICCV51070.2023.01812"},{"key":"2166_CR70","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., & Li, H. (2019). PointRCNN: 3D object proposal generation and detection from point cloud. In CVPR.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"2166_CR71","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.1109\/TRO.2021.3122069","volume":"38","author":"K Sirohi","year":"2021","unstructured":"Sirohi, K., Mohan, R., B\u00fcscher, D., Burgard, W., & Valada, A. (2021). Efficientlps: Efficient lidar panoptic segmentation. IEEE Transactions on Robotic, 38, 1894.","journal-title":"IEEE Transactions on Robotic"},{"key":"2166_CR72","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, Z., Zhao, S., Lin, Y., Lin, J., Wang, H., & Han, S. (2020). Searching efficient 3d architectures with sparse point-voxel convolution. In ECCV","DOI":"10.1007\/978-3-030-58604-1_41"},{"key":"2166_CR73","doi-asserted-by":"crossref","unstructured":"Teichman, A., Levinson, J., & Thrun, S. (2011). Towards 3D object recognition via classification of arbitrary object tracks. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA.2011.5979636"},{"issue":"7","key":"2166_CR74","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1177\/0278364912442751","volume":"31","author":"A Teichman","year":"2012","unstructured":"Teichman, A., & Thrun, S. (2012). Tracking-based semi-supervised learning. The International Journal of Robotics Research, 31(7), 804\u2013818.","journal-title":"The International Journal of Robotics Research"},{"key":"2166_CR75","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., & Guibas, L.J. (2019). Kpconv: Flexible and deformable convolution for point clouds. In CVPR.","DOI":"10.1109\/ICCV.2019.00651"},{"issue":"4","key":"2166_CR76","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. (1991). Toward autonomous driving: The cmu navlab: i: Perception. IEEE Expert, 6(4), 31\u201342.","journal-title":"IEEE Expert"},{"key":"2166_CR77","unstructured":"Wang, D.Z., Posner, I., & Newman, P. (2012). What could move? Finding cars, pedestrians and bicyclists in 3D laser data. In International Conference on Robotics and Automation."},{"key":"2166_CR78","doi-asserted-by":"crossref","unstructured":"Weng, Z., Ogut, M.G., Limonchik, S., & Yeung, S. (2021). Unsupervised discovery of the long-tail in instance segmentation using hierarchical self-supervision. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00263"},{"key":"2166_CR79","unstructured":"Wong, K., Wang, S., Ren, M., Liang, M., & Urtasun, R. (2020). Identifying unknown instances for autonomous driving. In Conference on Robot Learning, pp. 384\u2013393. PMLR."},{"key":"2166_CR80","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, X., Zhao, S., Yue, X., & Keutzer, K. (2019). Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In 2019 International Conference on Robotics and Automation (ICRA), pp. 4376\u20134382. IEEE.","DOI":"10.1109\/ICRA.2019.8793495"},{"issue":"2","key":"2166_CR81","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1109\/TIV.2022.3195426","volume":"8","author":"G Xian","year":"2022","unstructured":"Xian, G., Ji, C., Zhou, L., Chen, G., Zhang, J., Li, B., Xue, X., & Pu, J. (2022). Location-guided lidar-based panoptic segmentation for autonomous driving. IEEE Transactions on Intelligent Vehicles, 8(2), 1473\u20131483.","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"2166_CR82","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., & Pu, S. (2021). Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In CVPR.","DOI":"10.1109\/ICCV48922.2021.01572"},{"issue":"10","key":"2166_CR83","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. (2018). Second: Sparsely embedded convolutional detection. Sensors, 18(10), 3337.","journal-title":"Sensors"},{"key":"2166_CR84","doi-asserted-by":"crossref","unstructured":"Ye, M., Xu, S., Cao, T., & Chen, Q. (2021). Drinet: A dual-representation iterative learning network for point cloud segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 7447\u20137456.","DOI":"10.1109\/ICCV48922.2021.00735"},{"key":"2166_CR85","doi-asserted-by":"crossref","unstructured":"Yi, L., Gong, B., & Funkhouser, T. (2021). Complete & label: A domain adaptation approach to semantic segmentation of lidar point clouds. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15363\u201315373","DOI":"10.1109\/CVPR46437.2021.01511"},{"key":"2166_CR86","doi-asserted-by":"crossref","unstructured":"Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., & Naemura, T. (2019). Classification-reconstruction learning for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR.2019.00414"},{"key":"2166_CR87","doi-asserted-by":"crossref","unstructured":"Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., & Naemura, T. (2019). Classification-reconstruction learning for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR.2019.00414"},{"key":"2166_CR88","unstructured":"Zhan, X., Wang, Q., Huang, K.-h., Xiong, H., Dou, D., & Chan, A.B. (2022). A comparative survey of deep active learning. arXiv preprint arXiv:2203.13450"},{"key":"2166_CR89","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, A.J., Xiong, Y., Casas, S., Yang, B., Ren, M., & Urtasun, R. (2023). Towards unsupervised object detection from lidar point clouds. In CVPR.","DOI":"10.1109\/CVPR52729.2023.00899"},{"key":"2166_CR90","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, Z., Yu, Q., Yi, R., Xie, Y., & Ma, L. (2023). Lidar-camera panoptic segmentation via geometry-consistent and semantic-aware alignment. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3662\u20133671.","DOI":"10.1109\/ICCV51070.2023.00339"},{"key":"2166_CR91","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, X., & Huang, X. (2021). A technical survey and evaluation of traditional point cloud clustering methods for lidar panoptic segmentation. In ICCV Workshops.","DOI":"10.1109\/ICCVW54120.2021.00279"},{"key":"2166_CR92","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, X., & Huang, X. (2022). A divide-and-merge point cloud clustering algorithm for lidar panoptic segmentation. In International Conference on Robotics and Automation.","DOI":"10.1109\/ICRA46639.2022.9812058"},{"key":"2166_CR93","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, Y., & Foroosh, H. (2021). Panoptic-polarnet: Proposal-free lidar point cloud panoptic segmentation. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01299"},{"key":"2166_CR94","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhou, H., Wang, T., Hong, F., Ma, Y., Li, W., Li, H., & Lin, D. (2021). Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00981"},{"key":"2166_CR95","doi-asserted-by":"crossref","unstructured":"Zitnick, C.L., & Doll\u00e1r, P. (2014). Edge Boxes: Locating object proposals from edges. In ECCV.","DOI":"10.1007\/978-3-319-10602-1_26"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02166-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02166-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02166-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T10:03:58Z","timestamp":1740391438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02166-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":95,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2166"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02166-9","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,19]]},"assertion":[{"value":"15 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Code has been released along with the submission.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}]}}