{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:52:28Z","timestamp":1784137948028,"version":"3.55.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045454","type":"print"},{"value":"9783032045461","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04546-1_13","type":"book-chapter","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:54:00Z","timestamp":1757516040000},"page":"148-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LPSF-LiDARNet: Log-Polar Spatiotemporal Fusion-Based LiDAR Point Cloud Semantic Segmentation for Autonomous Driving"],"prefix":"10.1007","author":[{"given":"Yuchen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahe","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huangcheng","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongyao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenchao","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Hu, Q., et al.: RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11105\u201311114 (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: Rangenet ++: fast and accurate lidar semantic segmentation. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4213\u20134220 (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1534\u20131543 (2016)","DOI":"10.1109\/CVPR.2016.170"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKitti: a dataset for semantic scene understanding of lidar sequences. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9296\u20139306 (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"13_CR5","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS\u201917, pp. 5105\u20135114, Red Hook, NY, USA. Curran Associates Inc (2017)"},{"key":"13_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-58604-1_1","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Xu","year":"2020","unstructured":"Xu, C., et al.: SqueezeSegV3: spatially-adaptive convolution for efficient point-cloud segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 1\u201319. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_1"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Tchapmi, L., Choy, C., Armeni, I., Gwak, J., Savarese, S.: SegCloud: semantic segmentation of 3D point clouds. In: 2017 International Conference on 3D Vision (3DV), pp. 537\u2013547 (2017)","DOI":"10.1109\/3DV.2017.00067"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: Minkowski convolutional neural networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3070\u20133079 (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Shi, H., Lin, G., Wang, H., Hung, T.-Y., Wang, Z.: SpSequencenet: semantic segmentation network on 4D point clouds. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4573\u20134582 (2020)","DOI":"10.1109\/CVPR42600.2020.00463"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Li, J., Dai, H., Han, H., Ding, Y.: MSeg3D: multi-modal 3D semantic segmentation for autonomous driving. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21694\u201321704 (2023)","DOI":"10.1109\/CVPR52729.2023.02078"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Ouyang, Z., Dong, X., Zhang, C., Cui, J., Hu, Q., Niu, J.: Fast 3D point cloud target tracking based on polar-voxel encoding. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2439\u20132445 (2022)","DOI":"10.1109\/SMC53654.2022.9945125"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Cui, J., He, Y., Niu, J., Ouyang, Z., Xing, G.: $$\\alpha $$lidar: an adaptive high-resolution panoramic lidar system. In: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking, ACM MobiCom \u201924, pp. 1515\u20131529, New York, NY, USA. Association for Computing Machinery (2024)","DOI":"10.1145\/3636534.3690708"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Park, J., Koltun, V., Zhou, Q.-Y.: Tangent convolutions for dense prediction in 3D. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00409"},{"issue":"1","key":"13_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10514-021-09998-1","volume":"46","author":"RA Rosu","year":"2022","unstructured":"Rosu, R.A., Sch\u00fctt, P., Quenzel, J., Behnke, S.: LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices. Auton. Robots 46(1), 45\u201360 (2022)","journal-title":"Auton. Robots"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Bai, L., Huang, X.: FidNet: lidar point cloud semantic segmentation with fully interpolation decoding. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4453\u20134458 (2021)","DOI":"10.1109\/IROS51168.2021.9636385"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1109\/TIP.2025.3550011","volume":"34","author":"X Xiang","year":"2025","unstructured":"Xiang, X., Kong, L., Shuai, H., Liu, Q.: FRNet: frustum-range networks for scalable lidar segmentation. IEEE Trans. Image Process. 34, 2173\u20132186 (2025)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"13_CR17","first-page":"3101","volume":"35","author":"X Yan","year":"2021","unstructured":"Yan, X., et al.: Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion. Proc. AAAI Conf. Artif. Intell. 35(4), 3101\u20133109 (2021)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"10","key":"13_CR18","doi-asserted-by":"publisher","first-page":"6807","DOI":"10.1109\/TPAMI.2021.3098789","volume":"44","author":"X Zhu","year":"2022","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for lidar-based perception. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6807\u20136822 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Wu, X., et al.: Point transformer V3: simpler, faster, stronger. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4840\u20134851, Los Alamitos, CA, USA. IEEE Computer Society (2024)","DOI":"10.1109\/CVPR52733.2024.00463"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: RPVNet: a deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 16004\u201316013 (2021)","DOI":"10.1109\/ICCV48922.2021.01572"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04546-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:54:08Z","timestamp":1757516048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04546-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"ISBN":["9783032045454","9783032045461"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04546-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"11 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}