{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:26:20Z","timestamp":1777389980404,"version":"3.51.4"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"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":[[2023,4]]},"DOI":"10.1007\/s11263-022-01740-3","type":"journal-article","created":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T08:02:40Z","timestamp":1672473760000},"page":"938-967","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8387-3565","authenticated-orcid":false,"given":"Kangcheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"1740_CR1","doi-asserted-by":"crossref","unstructured":"Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., & Savarese, S. (2016). 3d semantic parsing of large-scale indoor spaces. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1534\u20131543).","DOI":"10.1109\/CVPR.2016.170"},{"key":"1740_CR2","doi-asserted-by":"crossref","unstructured":"Aubry, M., Schlickewei, U., & Cremers, D. (2011). The wave kernel signature: A quantum mechanical approach to shape analysis. In 2011 IEEE international conference on computer vision workshops (ICCV workshops) (pp. 1626\u20131633). IEEE.","DOI":"10.1109\/ICCVW.2011.6130444"},{"key":"1740_CR3","doi-asserted-by":"crossref","unstructured":"Beamer, S., Asanovic, K., & Patterson, D. (2012). Direction-optimizing breadth-first search. In SC\u201912: Proceedings of the international conference on high performance computing, networking, storage and analysis (pp. 1\u201310). IEEE.","DOI":"10.1109\/SC.2012.50"},{"key":"1740_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 Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 9297\u20139307).","DOI":"10.1109\/ICCV.2019.00939"},{"key":"1740_CR5","doi-asserted-by":"crossref","unstructured":"Behley, J., Steinhage, V., & Cremers, A. B. (2015) Efficient radius neighbor search in three-dimensional point clouds. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 3625\u20133630). IEEE.","DOI":"10.1109\/ICRA.2015.7139702"},{"issue":"8\u20139","key":"1740_CR6","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1177\/02783649211006735","volume":"40","author":"J Behley","year":"2021","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Gall, J., & Stachniss, C. (2021). Towards 3d lidar-based semantic scene understanding of 3d point cloud sequences: The semantickitti dataset. The International Journal of Robotics Research, 40(8\u20139), 959\u2013967.","journal-title":"The International Journal of Robotics Research"},{"key":"1740_CR7","doi-asserted-by":"crossref","unstructured":"Bronstein, M. M., & Kokkinos, I. (2010). Scale-invariant heat kernel signatures for non-rigid shape recognition. In 2010 IEEE computer society conference on computer vision and pattern recognition (pp. 1704\u20131711). IEEE.","DOI":"10.1109\/CVPR.2010.5539838"},{"key":"1740_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, Z., Zhang, B., Fok, W., Qi, X., Wu, & Y.C. (2023). Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection. In AAAI conference on artificial intelligence. AAAI.","DOI":"10.1609\/aaai.v37i1.25112"},{"key":"1740_CR9","doi-asserted-by":"crossref","unstructured":"Chen, D.Y., Tian, X.P., Shen, Y.T., & Ouhyoung, M. (2003). On visual similarity based 3d model retrieval. In Computer graphics forum (Vol.\u00a022, pp. 223\u2013232). Wiley Online Library.","DOI":"10.1111\/1467-8659.00669"},{"key":"1740_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, M., Hui, L., Xie, J., & Yang, J. (2021). Sspc-net: Semi-supervised semantic 3d point cloud segmentation network. In Proceedings of the AAAI conference on artificial intelligence (Vol.\u00a035, pp. 1140\u20131147).","DOI":"10.1609\/aaai.v35i2.16200"},{"key":"1740_CR11","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., & Savarese, S. (2019). 4d spatio-temporal convnets: Minkowski convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3075\u20133084).","DOI":"10.1109\/CVPR.2019.00319"},{"key":"1740_CR12","doi-asserted-by":"crossref","unstructured":"Chu, R., Ye, X., Liu, Z., Tan, X., Qi, X., Fu, C.W., & Jia, J. (2022)Twist: Two-way inter-label self-training for semi-supervised 3d instance segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 1100\u20131109).","DOI":"10.1109\/CVPR52688.2022.00117"},{"key":"1740_CR13","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Nie\u00dfner, M. (2017). Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5828\u20135839).","DOI":"10.1109\/CVPR.2017.261"},{"key":"1740_CR14","doi-asserted-by":"crossref","unstructured":"Eckart, B., Yuan, W., Liu, C., & Kautz, J. (2021). Self-supervised learning on 3d point clouds by learning discrete generative models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 8248\u20138257).","DOI":"10.1109\/CVPR46437.2021.00815"},{"key":"1740_CR15","doi-asserted-by":"crossref","unstructured":"Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016). Virtual worlds as proxy for multi-object tracking analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4340\u20134349).","DOI":"10.1109\/CVPR.2016.470"},{"key":"1740_CR16","doi-asserted-by":"crossref","unstructured":"Gebal, K., B\u00e6rentzen, J.A., Aan\u00e6s, H., & Larsen, R. (2009). Shape analysis using the auto diffusion function. In Computer graphics forum (Vol.\u00a028, pp. 1405\u20131413). Wiley Online Library.","DOI":"10.1111\/j.1467-8659.2009.01517.x"},{"issue":"11","key":"1740_CR17","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11), 1231\u20131237.","journal-title":"The International Journal of Robotics Research"},{"key":"1740_CR18","doi-asserted-by":"crossref","unstructured":"Gong, J., Xu, J., Tan, X., Song, H., Qu, Y., Xie, Y., & Ma, L. (2021). Omni-supervised point cloud segmentation via gradual receptive field component reasoning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11,673\u201311,682).","DOI":"10.1109\/CVPR46437.2021.01150"},{"key":"1740_CR19","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., & van\u00a0der Maaten, L. (2018) 3d semantic segmentation with submanifold sparse convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 9224\u20139232).","DOI":"10.1109\/CVPR.2018.00961"},{"key":"1740_CR20","doi-asserted-by":"crossref","unstructured":"Hackel, T., Savinov, N., Ladicky, L., Wegner, J. D., Schindler, K., & Pollefeys, M. (2017). Semantic3d. net: A new large-scale point cloud classification benchmark. arXiv preprint arXiv:1704.03847","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017"},{"key":"1740_CR21","doi-asserted-by":"crossref","unstructured":"He, T., Shen, C., & van\u00a0den Hengel, A. (2021). Dyco3d: Robust instance segmentation of 3d point clouds through dynamic convolution. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 354\u2013363).","DOI":"10.1109\/CVPR46437.2021.00042"},{"key":"1740_CR22","doi-asserted-by":"crossref","unstructured":"Hou, J., Dai, A., & Niessner, M. (2019) 3d-sis: 3d semantic instance segmentation of rgb-d scans. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (2019).","DOI":"10.1109\/CVPR.2019.00455"},{"key":"1740_CR23","doi-asserted-by":"crossref","unstructured":"Hou, J., Graham, B., Nie\u00dfner, M., & Xie, S. (2021). Exploring data-efficient 3d scene understanding with contrastive scene contexts. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 15,587\u201315,597).","DOI":"10.1109\/CVPR46437.2021.01533"},{"key":"1740_CR24","doi-asserted-by":"crossref","unstructured":"Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., & Schindler, K.(2021). Predator: Registration of 3d point clouds with low overlap. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4267\u20134276).","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"1740_CR25","unstructured":"Imani, E., & White, M. (2018). Improving regression performance with distributional losses. In International conference on machine learning (pp. 2157\u20132166). PMLR."},{"key":"1740_CR26","doi-asserted-by":"crossref","unstructured":"Jiang, L., Shi, S., Tian, Z., Lai, X., Liu, S., Fu, C.W., & Jia, J. (2021). Guided point contrastive learning for semi-supervised point cloud semantic segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 6423\u20136432).","DOI":"10.1109\/ICCV48922.2021.00636"},{"key":"1740_CR27","unstructured":"Jiang, M., Wu, Y., Zhao, T., Zhao, Z., & Lu, C. (2018). Pointsift: A sift-like network module for 3d point cloud semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition."},{"key":"1740_CR28","doi-asserted-by":"crossref","unstructured":"Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C.W., & Jia, J. (2020). Pointgroup: Dual-set point grouping for 3d instance segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4867\u20134876).","DOI":"10.1109\/CVPR42600.2020.00492"},{"key":"1740_CR29","doi-asserted-by":"crossref","unstructured":"Kundu, A., Yin, X., Fathi, A., Ross, D., Brewington, B., Funkhouser, T., & Pantofaru, C. (2020). Virtual multi-view fusion for 3d semantic segmentation. In European conference on computer vision (pp. 518\u2013535). Springer.","DOI":"10.1007\/978-3-030-58586-0_31"},{"key":"1740_CR30","doi-asserted-by":"crossref","unstructured":"Landrieu, L., & Boussaha, M. (2019). Point cloud oversegmentation with graph-structured deep metric learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 7440\u20137449).","DOI":"10.1109\/CVPR.2019.00762"},{"key":"1740_CR31","doi-asserted-by":"crossref","unstructured":"Landrieu, L., & Simonovsky, M. (2018) Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 4558\u20134567).","DOI":"10.1109\/CVPR.2018.00479"},{"issue":"4","key":"1740_CR32","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1137\/17M1113436","volume":"10","author":"L Landrieu","year":"2017","unstructured":"Landrieu, L., & Obozinski, G. (2017). Cut pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs. SIAM Journal on Imaging Sciences, 10(4), 1724\u20131766.","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"1740_CR33","doi-asserted-by":"crossref","unstructured":"Li, X., Sun, X., Meng, Y., Liang, J., Wu, F., & Li, J. (2019). Dice loss for data-imbalanced nlp tasks. arXiv preprint arXiv:1911.02855","DOI":"10.18653\/v1\/2020.acl-main.45"},{"key":"1740_CR34","doi-asserted-by":"crossref","unstructured":"Li, M., Xie, Y., Shen, Y., Ke, B., Qiao, R., Ren, B., Lin, S., Ma, L. (2022) Hybridcr: Weakly-supervised 3d point cloud semantic segmentation via hybrid contrastive regularization. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 14,930\u201314,939).","DOI":"10.1109\/CVPR52688.2022.01451"},{"key":"1740_CR35","doi-asserted-by":"crossref","unstructured":"Liu, K. (2022). A robust and efficient lidar-inertial-visual fused simultaneous localization and mapping system with loop closure. In 2022 12th international conference on CYBER technology in automation, control, and intelligent systems (CYBER) (pp. 1182\u20131187). IEEE.","DOI":"10.1109\/CYBER55403.2022.9907651"},{"key":"1740_CR36","unstructured":"Liu, K. (2022). An integrated lidar-slam system for complex environment with noisy point clouds. arXiv preprint arXiv:2212.05705"},{"key":"1740_CR37","doi-asserted-by":"crossref","unstructured":"Liu, K. (2022). Robust industrial uav\/ugv-based unsupervised domain adaptive crack recognitions with depth and edge awareness: From system and database constructions to real-site inspections. In Proceedings of the 30th ACM international conference on multimedia (pp. 5361\u20135370).","DOI":"10.1145\/3503161.3548304"},{"key":"1740_CR38","doi-asserted-by":"crossref","unstructured":"Liu, K. (2022). Semi-supervised confidence-level-based contrastive discrimination for class-imbalanced semantic segmentation. In 2022 12th International conference on CYBER technology in automation, control, and intelligent systems (CYBER) (pp. 1230\u20131235). IEEE.","DOI":"10.1109\/CYBER55403.2022.9907146"},{"key":"1740_CR39","doi-asserted-by":"crossref","unstructured":"Liu, K., & Ou, H. (2022). A light-weight lidar-inertial slam system with high efficiency and loop closure detection capacity. In 2022 International conference on advanced robotics and mechatronics (ICARM) (pp. 284\u2013289). IEEE.","DOI":"10.1109\/ICARM54641.2022.9959555"},{"key":"1740_CR40","doi-asserted-by":"crossref","unstructured":"Liu, K., & Ou, H. (2022). A light-weight lidar-inertial slam system with loop closing. arXiv preprint arXiv:2212.05743","DOI":"10.1109\/ICARM54641.2022.9959555"},{"key":"1740_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., & Pan, C. (2019). Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 8895\u20138904).","DOI":"10.1109\/CVPR.2019.00910"},{"key":"1740_CR42","doi-asserted-by":"crossref","unstructured":"Liu, K., Gao, Z., Lin, F., & Chen, B.M. (2020). Fg-net: Fast large-scale lidar point clouds understanding network leveraging correlatedfeature mining and geometric-aware modelling. arXiv preprint arXiv:2012.09439","DOI":"10.1109\/ICRA48506.2021.9561496"},{"key":"1740_CR43","doi-asserted-by":"crossref","unstructured":"Liu, K., Gao, Z., Lin, F., & Chen, B. M. (2021). Fg-conv: Large-scale lidar point clouds understanding leveraging feature correlation mining and geometric-aware modeling. In 2021 IEEE international conference on robotics and automation (ICRA) (pp. 12,896\u201312,902). IEEE.","DOI":"10.1109\/ICRA48506.2021.9561496"},{"key":"1740_CR44","doi-asserted-by":"crossref","unstructured":"Liu, K., Han, X., & Chen, B. M. (2019). Deep learning based automatic crack detection and segmentation for unmanned aerial vehicle inspections. In 2019 IEEE international conference on robotics and biomimetics (ROBIO) (pp. 381\u2013387). IEEE.","DOI":"10.1109\/ROBIO49542.2019.8961534"},{"key":"1740_CR45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., & Fu, C.-W. (2021). 3d-to-2d distillation for indoor scene parsing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4464\u20134474).","DOI":"10.1109\/CVPR46437.2021.00444"},{"key":"1740_CR46","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., Fu, & C. W. (2021). One thing one click: A self-training approach for weakly supervised 3d semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 1726\u20131736).","DOI":"10.1109\/CVPR46437.2021.00177"},{"key":"1740_CR47","doi-asserted-by":"crossref","unstructured":"Liu, K., Xiao, A., Huang, J., Cui, K., Xing, Y., & Lu, S. (2022). D-lc-nets: Robust denoising and loop closing networks for lidar slam in complicated circumstances with noisy point clouds. In IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 3097\u20133106).","DOI":"10.1109\/IROS47612.2022.9981388"},{"key":"1740_CR48","doi-asserted-by":"crossref","unstructured":"Liu, K., Yang, G., Zhang, J., Zhao, Z., Chen, X., Chen, B. M. (2022). Datasets and methods for boosting infrastructure inspection: A survey on defect segmentation and detection. In 2022 IEEE 17th international conference on control & automation (ICCA) (pp. 23\u201330). IEEE.","DOI":"10.1109\/ICCA54724.2022.9831925"},{"key":"1740_CR49","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhao, Y., Gao, Z., Chen, B. M. (2022). Weaklabel3d-net: A complete framework for real-scene lidar point clouds weakly supervised multi-tasks understanding. In 2022 international conference on robotics and automation (ICRA), pp. 5108\u20135115. IEEE.","DOI":"10.1109\/ICRA46639.2022.9811959"},{"key":"1740_CR50","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhao, Y., Nie, Q., Gao, Z., & Chen, B. M. (2022). Weakly supervised 3d scene segmentation with region-level boundary awareness and instance discrimination. In European conference on computer vision (pp. 37\u201355). Springer.","DOI":"10.1007\/978-3-031-19815-1_3"},{"key":"1740_CR51","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhao, Y., Nie, Q., Gao, Z., Chen, B. M. (2022). Ws3d supplementary material. In European conference on computer vision (ECCV) (pp. 37\u201355). Cham: Springer.","DOI":"10.1007\/978-3-031-19815-1_3"},{"key":"1740_CR52","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhou, X., Zhao, B., Ou, H., Chen, B. M. (2022). An integrated visual system for unmanned aerial vehicles following ground vehicles: Simulations and experiments. In 2022 IEEE 17th International Conference on Control & Automation (ICCA) (pp. 593\u2013598). IEEE.","DOI":"10.1109\/ICCA54724.2022.9831831"},{"issue":"1","key":"1740_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIE.2022.3198241","volume":"1","author":"K Liu","year":"2022","unstructured":"Liu, K., & Chen, B. M. (2022). Industrial uav-based unsupervised domain adaptive crack recognitions: From system setups to real-site infrastructural inspections. IEEE Transactions on Industrial Electronics, 1(1), 1\u201311.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"1","key":"1740_CR54","first-page":"1","volume":"1","author":"K Liu","year":"2022","unstructured":"Liu, K., Gao, Z., Lin, F., & Chen, B. M. (2022). Fg-net: A fast and accurate framework for large-scale lidar point cloud understanding. IEEE Transactions on Cybernetics, 1(1), 1\u201312.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"3","key":"1740_CR55","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1109\/TPWRS.2017.2761897","volume":"33","author":"K Liu","year":"2017","unstructured":"Liu, K., Qu, Y., Kim, H. M., & Song, H. (2017). Avoiding frequency second dip in power unreserved control during wind power rotational speed recovery. IEEE Transactions on Power Systems, 33(3), 3097\u20133106.","journal-title":"IEEE Transactions on Power Systems"},{"key":"1740_CR56","unstructured":"Luo, L., Tian, B., Zhao, H., & Zhou, G. (2021). Pointly-supervised 3d scene parsing with viewpoint bottleneck. arXiv preprint arXiv:2109.08553"},{"key":"1740_CR57","doi-asserted-by":"crossref","unstructured":"Papon, J., Abramov, A., Schoeler, M., & Worgotter, F. (2013). Voxel cloud connectivity segmentation-supervoxels for point clouds. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2027\u20132034).","DOI":"10.1109\/CVPR.2013.264"},{"key":"1740_CR58","doi-asserted-by":"crossref","unstructured":"Pham, Q. H., Nguyen, T., Hua, B. S., Roig, G., & Yeung, S. K. (2019). Jsis3d: Joint semantic-instance segmentation of 3d point clouds with multi-task pointwise networks and multi-value conditional random fields. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition) (pp. 8827\u20138836).","DOI":"10.1109\/CVPR.2019.00903"},{"key":"1740_CR59","doi-asserted-by":"crossref","unstructured":"Qi, C. R., Litany, O., He, K., Guibas, L. J. (2019). Deep hough voting for 3d object detection in point clouds. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9277\u20139286).","DOI":"10.1109\/ICCV.2019.00937"},{"key":"1740_CR60","unstructured":"Ren, J., Pan, L., Liu, Z. (2022). Benchmarking and analyzing point cloud classification under corruptions. ICML."},{"key":"1740_CR61","doi-asserted-by":"crossref","unstructured":"Rusu, R. B., & Cousins, S. (2011). 3d is here: Point cloud library (pcl). In 2011 IEEE international conference on robotics and automation (pp. 1\u20134). IEEE.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"1740_CR62","doi-asserted-by":"crossref","unstructured":"Rusu, R. B., Blodow, N., & Beetz, M. (2009). Fast point feature histograms (fpfh) for 3d registration. In 2009 IEEE international conference on robotics and automation (ICRA) (pp. 3212\u20133217). IEEE.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"1740_CR63","doi-asserted-by":"crossref","unstructured":"Rusu, R. B., Blodow, N., Marton, Z. C., & Beetz, M. (2008). Aligning point cloud views using persistent feature histograms. In 2008 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 3384\u20133391). IEEE.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"1740_CR64","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.cviu.2014.04.011","volume":"125","author":"S Salti","year":"2014","unstructured":"Salti, S., Tombari, F., & Di Stefano, L. (2014). Shot: Unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding, 125, 251\u2013264.","journal-title":"Computer Vision and Image Understanding"},{"key":"1740_CR65","unstructured":"Shi, X., Xu, X., Chen, K., Cai, L., Foo, C.S., & Jia, K. (2021). Label-efficient point cloud semantic segmentation: An active learning approach. arXiv preprint arXiv:2101.06931"},{"key":"1740_CR66","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., & Caine, B., et\u00a0al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2446\u20132454).","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"1740_CR67","doi-asserted-by":"crossref","unstructured":"Sun, J., Ovsjanikov, M., & Guibas, L. (2009). A concise and provably informative multi-scale signature based on heat diffusion. In Computer graphics forum (Vol.\u00a028, pp. 1383\u20131392). Wiley Online Library.","DOI":"10.1111\/j.1467-8659.2009.01515.x"},{"key":"1740_CR68","doi-asserted-by":"crossref","unstructured":"Tang, W., & Zou, D. (2022). Multi-instance point cloud registration by efficient correspondence clustering. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 6667\u20136676).","DOI":"10.1109\/CVPR52688.2022.00655"},{"key":"1740_CR69","doi-asserted-by":"crossref","unstructured":"Thabet, A., Alwassel, H., & Ghanem, B. (2020). Self-supervised learning of local features in 3d point clouds. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (pp. 938\u2013939).","DOI":"10.1109\/CVPRW50498.2020.00477"},{"key":"1740_CR70","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., & Di\u00a0Stefano, L. (2010). Unique signatures of histograms for local surface description. In European conference on computer vision (ECCV) (pp. 356\u2013369). Springer.","DOI":"10.1007\/978-3-642-15558-1_26"},{"issue":"2","key":"1740_CR71","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International Journal of Computer Vision, 104(2), 154\u2013171.","journal-title":"International Journal of Computer Vision"},{"key":"1740_CR72","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, Q., Yue, X., Lasenby, J., & Kusner, M. J. (2021). Unsupervised point cloud pre-training via occlusion completion. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9782\u20139792).","DOI":"10.1109\/ICCV48922.2021.00964"},{"key":"1740_CR73","unstructured":"Wang, H., Rong, X., Yang, L., Feng, J., Xiao, J., Tian, Y.(2020). Weakly supervised semantic segmentation in 3d graph-structured point clouds of wild scenes. arXiv preprint arXiv:2004.12498"},{"key":"1740_CR74","doi-asserted-by":"crossref","unstructured":"Wen, X., Han, Z., Youk, G., Liu, Y. S. (2020). Cf-sis: Semantic-instance segmentation of 3d point clouds by context fusion with self-attention. In Proceedings of the 28th ACM international conference on multimedia (pp. 1661\u20131669).","DOI":"10.1145\/3394171.3413829"},{"key":"1740_CR75","doi-asserted-by":"crossref","unstructured":"Wu, T. H., Liu, Y. C., Huang, Y. K., Lee, H. Y., Su, H. T., Huang, P. C., Hsu, W. H. (2021). Redal: Region-based and diversity-aware active learning for point cloud semantic segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 15,510\u201315,519).","DOI":"10.1109\/ICCV48922.2021.01522"},{"key":"1740_CR76","doi-asserted-by":"crossref","unstructured":"Xie, S., Gu, J., Guo, D., Qi, C. R., Guibas, L., & Litany, O. (2020). Pointcontrast: Unsupervised pre-training for 3d point cloud understanding. In European conference on computer vision (ECCV) (pp. 574\u2013591). Springer.","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"1740_CR77","doi-asserted-by":"crossref","unstructured":"Xu, X., & Lee, G. H. (2020). Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13,706\u201313,715).","DOI":"10.1109\/CVPR42600.2020.01372"},{"key":"1740_CR78","doi-asserted-by":"crossref","unstructured":"Yang, G., Liu, K., Zhao, Z., Zhang, J., Chen, X., & Chen, B. M. (2022). Datasets and methods for boosting infrastructure inspection: A survey on defect classification. In 2022 IEEE 17th international conference on control and automation (ICCA) (pp. 15\u201322). IEEE.","DOI":"10.1109\/ICCA54724.2022.9831922"},{"key":"1740_CR79","doi-asserted-by":"publisher","first-page":"129,226","DOI":"10.1016\/j.conbuildmat.2022.129226","volume":"356","author":"G Yang","year":"2022","unstructured":"Yang, G., Liu, K., Zhang, J., Zhao, B., Zhao, Z., Chen, X., & Chen, B. M. (2022). Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection. Construction and Building Materials, 356, 129,226.","journal-title":"Construction and Building Materials"},{"key":"1740_CR80","doi-asserted-by":"crossref","unstructured":"Ye, S., Chen, D., Han, S., & Liao, J. (2021). Learning with noisy labels for robust point cloud segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 6443\u20136452).","DOI":"10.1109\/ICCV48922.2021.00638"},{"key":"1740_CR81","unstructured":"Yuzhi, Z., Lai-Man, P., Xuehui, W., Kangcheng, L., Yujia, Z., Wing-Yin, Y., Pengfei, X., & Jingjing, X. (2020). Legacy photo editing with learned noise prior. arXiv\u00a0preprint arXiv:2011.11309"},{"key":"1740_CR82","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qu, Y., Xie, Y., Li, Z., Zheng, S., & Li, C. (2021). Perturbed self-distillation: Weakly supervised large-scale point cloud semantic segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 15,520\u201315,528).","DOI":"10.1109\/ICCV48922.2021.01523"},{"issue":"6","key":"1740_CR83","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.cag.2013.05.008","volume":"37","author":"J Zhang","year":"2013","unstructured":"Zhang, J., Cao, J., Liu, X., Wang, J., Liu, J., & Shi, X. (2013). Point cloud normal estimation via low-rank subspace clustering. Computers & Graphics, 37(6), 697\u2013706.","journal-title":"Computers & Graphics"},{"key":"1740_CR84","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Po, L. M., Lin, T., Wang, X., Liu, K., Zhang, Y., Yu, W. Y., Xian, P., & Xiong, J. (2020). Legacy photo editing with learned noise prior. arXiv preprint arXiv:2011.11309","DOI":"10.1109\/WACV48630.2021.00215"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01740-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-022-01740-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01740-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T20:06:57Z","timestamp":1701634017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-022-01740-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,31]]},"references-count":84,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1740"],"URL":"https:\/\/doi.org\/10.1007\/s11263-022-01740-3","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,31]]},"assertion":[{"value":"2 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}