{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:44:08Z","timestamp":1781009048642,"version":"3.54.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"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":["Vis Comput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00371-024-03344-z","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T03:10:07Z","timestamp":1712545807000},"page":"517-534","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learnable scene prior for point cloud semantic segmentation"],"prefix":"10.1007","volume":"41","author":[{"given":"Yuanhao","family":"Chai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyu","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiachen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhuang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"issue":"4\u20135","key":"3344_CR1","doi-asserted-by":"publisher","first-page":"e1959","DOI":"10.1002\/cav.1959","volume":"31","author":"E Ertugrul","year":"2020","unstructured":"Ertugrul, E., Zhang, H., Zhu, F., Lu, P., Li, P., Sheng, B., Wu, E.: Embedding 3d models in offline physical environments. Comput. Anim. Virtual Worlds 31(4\u20135), e1959 (2020)","journal-title":"Comput. Anim. Virtual Worlds"},{"key":"3344_CR2","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.cagd.2018.03.021","volume":"62","author":"B Sheng","year":"2018","unstructured":"Sheng, B., Liu, B., Li, P., Fu, H., Ma, L., Wu, E.: Accelerated robust boolean operations based on hybrid representations. Comput. Aided Geom. Des. 62, 133\u2013153 (2018)","journal-title":"Comput. Aided Geom. Des."},{"issue":"3","key":"3344_CR3","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1109\/TCYB.2020.2988792","volume":"51","author":"B Sheng","year":"2020","unstructured":"Sheng, B., Li, P., Zhang, Y., Mao, L., Chen, C.P.: Greensea: visual soccer analysis using broad learning system. IEEE Trans. Cybern. 51(3), 1463\u20131477 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"3344_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.gmod.2018.03.001","volume":"97","author":"B Sheng","year":"2018","unstructured":"Sheng, B., Li, P., Fu, H., Ma, L., Wu, E.: Efficient non-incremental constructive solid geometry evaluation for triangular meshes. Graph. Models 97, 1\u201316 (2018)","journal-title":"Graph. Models"},{"key":"3344_CR5","doi-asserted-by":"crossref","unstructured":"Gong, J., Xu, J., Tan, X., Song, H., Qu, Y., Xie, Y., Ma, L.: 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\u00a0673\u201311\u00a0682 (2021)","DOI":"10.1109\/CVPR46437.2021.01150"},{"key":"3344_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3319470","author":"X Tan","year":"2023","unstructured":"Tan, X., Ma, Q., Gong, J., Xu, J., Zhang, Z., Song, H., Qu, Y., Xie, Y., Ma, L.: Positive-negative receptive field reasoning for omni-supervised 3d segmentation. IEEE Trans. Pattern Analy. Mach. Intell. (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3319470","journal-title":"IEEE Trans. Pattern Analy. Mach. Intell."},{"key":"3344_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-023-02921-y","author":"J Jiang","year":"2023","unstructured":"Jiang, J., Lu, X., Ouyang, W., Wang, M.: Unsupervised contrastive learning with simple transformation for 3d point cloud data. Visual Comput. (2023). https:\/\/doi.org\/10.1007\/s00371-023-02921-y","journal-title":"Visual Comput."},{"key":"3344_CR8","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., Girshick, R.: Pointrend: Image segmentation as rendering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9799\u20139808 (2020)","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"3344_CR9","doi-asserted-by":"publisher","first-page":"9085","DOI":"10.1109\/TIP.2021.3122004","volume":"30","author":"X Tan","year":"2021","unstructured":"Tan, X., Xu, K., Cao, Y., Zhang, Y., Ma, L., Lau, R.W.: Night-time scene parsing with a large real dataset. IEEE Trans. Image Process. 30, 9085\u20139098 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3344_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3352232","author":"F Qi","year":"2024","unstructured":"Qi, F., Tan, X., Zhang, Z., Chen, M., Xie, Y., Ma, L.: Glass makes blurs: learning the visual blurriness for glass surface detection. IEEE Trans. Ind. Inf. (2024). https:\/\/doi.org\/10.1109\/TII.2024.3352232","journal-title":"IEEE Trans. Ind. Inf."},{"key":"3344_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3314973","author":"J Jiang","year":"2023","unstructured":"Jiang, J., Lu, X., Zhao, L., Dazaley, R., Wang, M.: Masked autoencoders in 3d point cloud representation learning. IEEE Trans. Multimedia (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3314973","journal-title":"IEEE Trans. Multimedia"},{"key":"3344_CR12","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3d shape recognition. In: The IEEE International Conference on Computer Vision (ICCV), pp. 945\u2013953 (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"3344_CR13","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., van der Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 9224\u20139232 (2018)","DOI":"10.1109\/CVPR.2018.00961"},{"key":"3344_CR14","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 652\u2013660 (2017)"},{"key":"3344_CR15","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(NIPS), pp. 5099\u20135108 (2017)"},{"key":"3344_CR16","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 9621\u20139630 (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"3344_CR17","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s41095-021-0244-6","volume":"8","author":"J Gong","year":"2022","unstructured":"Gong, J., Ye, Z., Ma, L.: Neighborhood co-occurrence modeling in 3d point cloud segmentation. Comput. Vis. Media 8, 303\u2013315 (2022)","journal-title":"Comput. Vis. Media"},{"key":"3344_CR18","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: Spidercnn: Deep learning on point sets with parameterized convolutional filters. In: Proceedings of the European conference on computer vision (ECCV), pp. 87\u2013102 (2018)","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"3344_CR19","doi-asserted-by":"crossref","unstructured":"Gupta, S., Arbelaez, P., Malik, J.: Perceptual organization and recognition of indoor scenes from rgb-d images. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 564\u2013571 (2013)","DOI":"10.1109\/CVPR.2013.79"},{"key":"3344_CR20","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., Ilic, S.: Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors. In: Proceedings of the European conference on computer vision (ECCV), pp. 602\u2013618 (2018)","DOI":"10.1007\/978-3-030-01228-1_37"},{"issue":"2","key":"3344_CR21","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1109\/TCYB.2017.2778764","volume":"49","author":"Z Han","year":"2017","unstructured":"Han, Z., Liu, Z., Han, J., Vong, C.-M., Bu, S., Chen, C.P.: Unsupervised learning of 3-d local features from raw voxels based on a novel permutation voxelization strategy. IEEE Trans. Cybern. 49(2), 481\u2013494 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"3344_CR22","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Nie\u00dfner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3dmatch: learning local geometric descriptors from rgb-d reconstructions. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 1802\u20131811 (2017)","DOI":"10.1109\/CVPR.2017.29"},{"key":"3344_CR23","doi-asserted-by":"crossref","unstructured":"Shen, Y., Feng, C., Yang, Y., Tian, D.: Mining point cloud local structures by kernel correlation and graph pooling. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 4548\u20134557 (2018)","DOI":"10.1109\/CVPR.2018.00478"},{"key":"3344_CR24","doi-asserted-by":"crossref","unstructured":"Tsin, Y., Kanade, T.: A correlation-based approach to robust point set registration. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp. 558\u2013569 (2004)","DOI":"10.1007\/978-3-540-24672-5_44"},{"key":"3344_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Fu, C.-W., Jia, J.: Pointweb: enhancing local neighborhood features for point cloud processing. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 5565\u20135573 (2019)","DOI":"10.1109\/CVPR.2019.00571"},{"key":"3344_CR26","doi-asserted-by":"crossref","unstructured":"Xu, J., Gong, J., Zhou, J., Tan, X., Xie, Y., Ma, L.: Sceneencoder: scene-aware semantic segmentation of point clouds with a learnable scene descriptor. In: Proceedings of the 29th international joint conference on artificial intelligence (IJCAI) (2020)","DOI":"10.24963\/ijcai.2020\/84"},{"key":"3344_CR27","unstructured":"Jiang, L., Zhao, H., Liu, S., Shen, X., Fu, C.-W., Jia, J.: Hierarchical point-edge interaction network for point cloud semantic segmentation. In: The IEEE international conference on computer vision (ICCV), pp. 10\u00a0433\u201310\u00a0441 (2019)"},{"key":"3344_CR28","doi-asserted-by":"crossref","unstructured":"Schult, J., Engelmann, F., Kontogianni, T., Leibe, B.: Dualconvmesh-net: joint geodesic and euclidean convolutions on 3d meshes. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), (2020)","DOI":"10.1109\/CVPR42600.2020.00864"},{"key":"3344_CR29","doi-asserted-by":"crossref","unstructured":"Le, T., Duan, Y.: Pointgrid: a deep network for 3d shape understanding. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 9204\u20139214 (2018)","DOI":"10.1109\/CVPR.2018.00959"},{"key":"3344_CR30","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.1007\/s00371-023-02922-x","volume":"39","author":"Y Qin","year":"2023","unstructured":"Qin, Y., Chi, X., Sheng, B., Lau, R.W.: Guiderender: large-scale scene navigation based on multi-modal view frustum movement prediction. Vis. Comput. 39, 3597 (2023)","journal-title":"Vis. Comput."},{"key":"3344_CR31","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: Voxnet: a 3d convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp. 922\u2013928 (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"3344_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: Voxelnet: end-to-end learning for point cloud based 3d object detection. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 4490\u20134499 (2018)","DOI":"10.1109\/CVPR.2018.00472"},{"key":"3344_CR33","doi-asserted-by":"crossref","unstructured":"Lin, Y., Yan, Z., Huang, H., Du, D., Liu, L., Cui, S., Han, X.: Fpconv: learning local flattening for point convolution. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 4293\u20134302 (2020)","DOI":"10.1109\/CVPR42600.2020.00435"},{"key":"3344_CR34","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: The IEEE international conference on computer vision (ICCV), pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"3344_CR35","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhao, L., Lu, X., Hu, W., Razzak, I., Wang, M.: Dhgcn: dynamic hop graph convolution network for self-supervised point cloud learning. arXiv preprint arXiv:2401.02610 (2024)","DOI":"10.1609\/aaai.v38i11.29185"},{"key":"3344_CR36","doi-asserted-by":"crossref","unstructured":"Liu, J., Ni, B., Li, C., Yang, J., Tian, Q.: Dynamic points agglomeration for hierarchical point sets learning. In: The IEEE International conference on computer vision (ICCV), (2019)","DOI":"10.1109\/ICCV.2019.00764"},{"key":"3344_CR37","doi-asserted-by":"crossref","unstructured":"Yan, X., Zheng, C., Li, Z., Wang, S., Cui, S.: Pointasnl: robust point clouds processing using nonlocal neural networks with adaptive sampling. In: The IEEE\/CVF Conference on computer vision and pattern recognition (cvpr) (2020)","DOI":"10.1109\/CVPR42600.2020.00563"},{"key":"3344_CR38","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., Markham, A.: Randla-net: efficient semantic segmentation of large-scale point clouds. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"3344_CR39","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: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 5828\u20135839 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"3344_CR40","doi-asserted-by":"crossref","unstructured":"Armeni, I., Sener, Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 1534\u20131543 (2016)","DOI":"10.1109\/CVPR.2016.170"},{"key":"3344_CR41","unstructured":"Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al.: Shapenet: an information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"issue":"5","key":"3344_CR42","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/34.765655","volume":"21","author":"AE Johnson","year":"1999","unstructured":"Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433\u2013449 (1999)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"3344_CR43","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1016\/j.patrec.2007.02.009","volume":"28","author":"H Chen","year":"2007","unstructured":"Chen, H., Bhanu, B.: 3d free-form object recognition in range images using local surface patches. Pattern Recogn. Lett. 28(10), 1252\u20131262 (2007)","journal-title":"Pattern Recogn. Lett."},{"key":"3344_CR44","unstructured":"Zhou, H., Chen, H., Feng, Y., Wang, Q., Qin, J., Xie, H., Wang, F.L., Wei, M., Wang, J.: Geometry and learning co-supported normal estimation for unstructured point cloud. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 13\u00a0238\u201313\u00a0247 (2020)"},{"key":"3344_CR45","doi-asserted-by":"crossref","unstructured":"Choy, C., Park, J., Koltun, V.: Fully convolutional geometric features. In: The IEEE international conference on computer vision (ICCV), pp. 8958\u20138966 (2019)","DOI":"10.1109\/ICCV.2019.00905"},{"key":"3344_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, L., Tao, W.: Jsnet: joint instance and semantic segmentation of 3d point clouds. In: AAAI, pp. 12\u00a0951\u201312\u00a0958 (2020)","DOI":"10.1609\/aaai.v34i07.6994"},{"issue":"2","key":"3344_CR47","first-page":"1424","volume":"35","author":"J Gong","year":"2021","unstructured":"Gong, J., Xu, J., Tan, X., Zhou, J., Qu, Y., Xie, Y., Ma, L.: Boundary-aware geometric encoding for semantic segmentation of point clouds. Proc. AAAI Conf. Artif. Intell. 35(2), 1424\u20131432 (2021)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"3344_CR48","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. In: Advances in neural information processing systems (NIPS), pp. 820\u2013830 (2018)"},{"key":"3344_CR49","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhang, H., Yi, L., Funkhouser, T., Nie\u00dfner, M., Guibas, L.J.: Texturenet: consistent local parametrizations for learning from high-resolution signals on meshes. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 4440\u20134449 (2019)","DOI":"10.1109\/CVPR.2019.00457"},{"key":"3344_CR50","doi-asserted-by":"crossref","unstructured":"Lei, H., Akhtar, N., Mian, A.: Seggcn: efficient 3d point cloud segmentation with fuzzy spherical kernel. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 11\u00a0611\u201311\u00a0620 (2020)","DOI":"10.1109\/CVPR42600.2020.01163"},{"key":"3344_CR51","doi-asserted-by":"publisher","first-page":"3664","DOI":"10.1109\/TPAMI.2020.2983410","volume":"43","author":"H Lei","year":"2020","unstructured":"Lei, H., Akhtar, N., Mian, A.: Spherical kernel for efficient graph convolution on 3d point clouds. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3664 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3344_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, C., Zheng, L., Xu, K.: Fusion-aware point convolution for online semantic 3d scene segmentation. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 4534\u20134543 (2020)","DOI":"10.1109\/CVPR42600.2020.00459"},{"key":"3344_CR53","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1109\/TIFS.2020.3036800","volume":"16","author":"H Li","year":"2021","unstructured":"Li, H., Chen, Y., Tao, D., Yu, Z., Qi, G.: Attribute-aligned domain-invariant feature learning for unsupervised domain adaptation person re-identification. IEEE Trans. Inf. For. Secur. 16, 1480\u20131494 (2021)","journal-title":"IEEE Trans. Inf. For. Secur."},{"issue":"1","key":"3344_CR54","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/TCSVT.2021.3056726","volume":"32","author":"W Shi","year":"2022","unstructured":"Shi, W., Xu, J., Zhu, D., Zhang, G., Wang, X., Li, J., Zhang, X.: Rgb-d semantic segmentation and label-oriented voxelgrid fusion for accurate 3d semantic mapping. IEEE Trans. Circuits Syst. Video Technol. 32(1), 183\u2013197 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3344_CR55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3155282","author":"Z Du","year":"2022","unstructured":"Du, Z., Ye, H., Cao, F.: A novel local-global graph convolutional method for point cloud semantic segmentation. IEEE Trans. Neural Netw. Learn. Syst. (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3155282","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3344_CR56","doi-asserted-by":"crossref","unstructured":"Huang, Q., Wang, W., Neumann, U.: Recurrent slice networks for 3d segmentation of point clouds. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 2626\u20132635 (2018)","DOI":"10.1109\/CVPR.2018.00278"},{"key":"3344_CR57","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhang, Q., Ni, B., Li, L., Liu, J., Zhou, M., Tian, Q.: Modeling point clouds with self-attention and gumbel subset sampling. In: The IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 3323\u20133332 (2019)","DOI":"10.1109\/CVPR.2019.00344"},{"key":"3344_CR58","first-page":"4571","volume":"32","author":"X Wang","year":"2019","unstructured":"Wang, X., He, J., Ma, L.: Exploiting local and global structure for point cloud semantic segmentation with contextual point representations. Adv. Neural Inf. Process. Syst. 32, 4571\u20134581 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"3344_CR59","doi-asserted-by":"crossref","unstructured":"Xu, Q., Sun, X., Wu, C.-Y., Wang, P., Neumann, U.: Grid-gcn for fast and scalable point cloud learning. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 5661\u20135670 (2020)","DOI":"10.1109\/CVPR42600.2020.00570"},{"key":"3344_CR60","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B.M., Hee Lee, G.: So-net: self-organizing network for point cloud analysis. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 9397\u20139406 (2018)","DOI":"10.1109\/CVPR.2018.00979"},{"issue":"4","key":"3344_CR61","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1145\/3197517.3201301","volume":"37","author":"M Atzmon","year":"2018","unstructured":"Atzmon, M., Maron, H., Lipman, Y.: Point convolutional neural networks by extension operators. ACM Trans. Graph. 37(4), 71 (2018)","journal-title":"ACM Trans. Graph."},{"key":"3344_CR62","doi-asserted-by":"crossref","unstructured":"Rao, Y., Lu, J., Zhou, J.: Spherical fractal convolutional neural networks for point cloud recognition. In: The IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 452\u2013460 (2019)","DOI":"10.1109\/CVPR.2019.00054"},{"key":"3344_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, B.-S., Yeung, S.-K.: Shellnet: efficient point cloud convolutional neural networks using concentric shells statistics. In: The IEEE international conference on computer vision (ICCV), pp. 1607\u20131616 (2019)","DOI":"10.1109\/ICCV.2019.00169"},{"key":"3344_CR64","doi-asserted-by":"publisher","first-page":"4599","DOI":"10.1109\/TCSVT.2021.3132047","volume":"32","author":"Z Song","year":"2021","unstructured":"Song, Z., Zhao, L., Zhou, J.: Learning hybrid semantic affinity for point cloud segmentation. IEEE Trans. Circuits Syst. Video Technol. 32, 4599 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03344-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03344-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03344-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T13:00:38Z","timestamp":1737723638000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03344-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3344"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03344-z","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"26 February 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}