{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:32:34Z","timestamp":1787157154277,"version":"build-2736575974"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Foundation of President of Hebei University","award":["XZJJ202213"],"award-info":[{"award-number":["XZJJ202213"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s11554-026-01948-3","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T13:52:44Z","timestamp":1785937964000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LERPNet: a lightweight and efficient range view point cloud semantic segmentation network"],"prefix":"10.1007","volume":"23","author":[{"given":"Yadong","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenling","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijun","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"1948_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113816","volume":"165","author":"C Badue","year":"2021","unstructured":"Badue, C., Guidolini, R., Carneiro, R.V., et al.: Self-driving cars: a survey. Expert Syst. Appl. 165, 113816 (2021)","journal-title":"Expert Syst. Appl."},{"key":"1948_CR2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.54254\/2755-2721\/2026.TJ28614","volume":"190","author":"Y Wu","year":"2025","unstructured":"Wu, Y.: Comparative analysis of LiDAR, radar, and camera sensors for autonomous perception: principles, challenges, and fusion strategies. Appl. Comput. Eng. 190, 74\u201380 (2025)","journal-title":"Appl. Comput. Eng."},{"key":"1948_CR3","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1038\/s44287-025-00176-4","volume":"2","author":"J Ibanez-Guzman","year":"2025","unstructured":"Ibanez-Guzman, J., Li, Y.: LiDAR and cameras in autonomous driving. Nature Rev. Electr. Eng. 2, 515\u2013516 (2025)","journal-title":"Nature Rev. Electr. Eng."},{"key":"1948_CR4","doi-asserted-by":"crossref","unstructured":"Wu, D., Liang, Z., Chen, G.: Deep learning for LiDAR-only and LiDAR-fusion 3D perception: a survey. Intell Robot (2022)","DOI":"10.20517\/ir.2021.20"},{"key":"1948_CR5","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of CVPR, pp. 652\u2013660 (2017)"},{"key":"1948_CR6","doi-asserted-by":"crossref","unstructured":"Behley, J., Garcia, M., Milioto, A. et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: Proceedings of ICCV, pp. 9297\u20139307 (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"issue":"12","key":"1948_CR7","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2021","unstructured":"Guo, Y., Wang, H., Hu, Q., et al.: Deep learning for 3D point clouds: a survey. IEEE TPAMI 43(12), 4338\u20134364 (2021)","journal-title":"IEEE TPAMI"},{"key":"1948_CR8","doi-asserted-by":"crossref","unstructured":"Shen, J., Liu, N., Sun, H., et al.: Lightweight semantic feature extraction model with direction awareness for aerial traffic object detection. IEEE Trans. Intell. Transp. Syst. (2025)","DOI":"10.1109\/TITS.2025.3642410"},{"key":"1948_CR9","doi-asserted-by":"crossref","unstructured":"Shen, J., Liu, N., Sun, H., et al.: An algorithm based on lightweight semantic features for ancient mural element object detection. npj Heritage Sci., 13(1), 70 (2025)","DOI":"10.1038\/s40494-025-01565-6"},{"key":"1948_CR10","doi-asserted-by":"crossref","unstructured":"Li, D., Jin, Z., Guan, C., et al.: KACNet: enhancing CNN feature representation with Kolmogorov\u2013Arnold networks for medical image segmentation and classification. Inf. Sci., 122760 (2025)","DOI":"10.1016\/j.ins.2025.122760"},{"issue":"12","key":"1948_CR11","doi-asserted-by":"publisher","first-page":"24330","DOI":"10.1109\/TITS.2022.3203715","volume":"23","author":"J Shen","year":"2022","unstructured":"Shen, J., Zhou, W., Liu, N., et al.: An anchor-free lightweight deep convolutional network for vehicle detection in aerial images. IEEE Trans. Intell. Transp. Syst. 23(12), 24330\u201324342 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1948_CR12","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., Guibas, L.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"1948_CR13","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., et al.: RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11108\u201311117 (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"1948_CR14","doi-asserted-by":"crossref","unstructured":"Qian, G., Li, Y., Peng, H., Wu, J., Zhang, P.: PointNeXt: revisiting PointNet++ with improved training and scaling strategies. Advances in Neural Information Processing Systems (NeurIPS) 35, 23192\u201323204 (2022)","DOI":"10.52202\/068431-1685"},{"key":"1948_CR15","doi-asserted-by":"crossref","unstructured":"Puy, G., Boulch, A., Marlet, R.: Using a waffle iron for automotive point cloud semantic segmentation. In: Proceedings of IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3379\u20133389 (2023)","DOI":"10.1109\/ICCV51070.2023.00313"},{"key":"1948_CR16","doi-asserted-by":"crossref","unstructured":"Wu, X., Jiang, L., Wang, P.-S., Liu, Z., Liu, X., Qiao, Y., Ouyang, W., He, T., Zhao, H.: Point transformer v3: simpler, faster, stronger. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4840\u20134851 (2024)","DOI":"10.1109\/CVPR52733.2024.00463"},{"key":"1948_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhou, H., Wang, T., Hong, F., Ma, Y., Li, W., Li, H., Lin, D.: Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9939\u20139948 (2021)","DOI":"10.1109\/CVPR46437.2021.00981"},{"key":"1948_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2024.104231","volume":"250","author":"GK Erabati","year":"2025","unstructured":"Erabati, G.K., Araujo, H.: RetSeg3D: retention-based 3D semantic segmentation for autonomous driving. Comput. Vis. Image Underst. 250, 104231 (2025)","journal-title":"Comput. Vis. Image Underst."},{"key":"1948_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Bai, L., Huang, X.: FidNet: LiDAR point cloud semantic segmentation with fully interpolation decoding. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4453\u20134458 (2021)","DOI":"10.1109\/IROS51168.2021.9636385"},{"key":"1948_CR20","doi-asserted-by":"crossref","unstructured":"Cheng, H.-X., Han, X.-F., Xiao, G.-Q.: CENet: toward concise and efficient LiDAR semantic segmentation for autonomous driving. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2022)","DOI":"10.1109\/ICME52920.2022.9859693"},{"key":"1948_CR21","doi-asserted-by":"crossref","unstructured":"Li, R., Li, S., Chen, X., Wang, Z., Zhang, J.: TFNet: exploiting temporal cues for fast and accurate LiDAR semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4547\u20134556 (2024)","DOI":"10.1109\/CVPRW63382.2024.00457"},{"key":"1948_CR22","doi-asserted-by":"crossref","unstructured":"Xu, X., Kong, L., Shuai, H., et al.: \u201cFRNet: frustum-range networks for scalable LiDAR segmentation. IEEE Trans. Image Process. (2025)","DOI":"10.1109\/TIP.2025.3550011"},{"key":"1948_CR23","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., Li, R., Jia, K., et al.: Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation. In: Proceedings of IEEE\/CVF International Conference Computer Visison (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01597"},{"key":"1948_CR24","doi-asserted-by":"crossref","unstructured":"Wu, X., Hou, Y., Huang, X., Lin, B., He, T., Zhu, X., Ma, Y., Wu, B., Liu, H., Cai, D.: TASeg: temporal aggregation network for LiDAR semantic segmentation. In: Proceedings of IEEE\/CVF Conference Computer Vision Pattern Recognition (CVPR) (2024)","DOI":"10.1109\/CVPR52733.2024.01450"},{"key":"1948_CR25","doi-asserted-by":"publisher","unstructured":"He, Y., Li, G., Shao, Y., Wang, J., Chen, Y., Liu, S.: A point cloud compression framework via spherical projection. In: Proceedings of IEEE International Conference Visual Communication Image Processing (VCIP), pp. 62\u201365 (2020). https:\/\/doi.org\/10.1109\/VCIP49819.2020.9301809","DOI":"10.1109\/VCIP49819.2020.9301809"},{"issue":"12","key":"1948_CR26","doi-asserted-by":"publisher","first-page":"4603","DOI":"10.1109\/TCSVT.2021.3098832","volume":"31","author":"F Song","year":"2021","unstructured":"Song, F., Shao, Y., Gao, W., et al.: Layer-wise geometry aggregation framework for lossless LiDAR point cloud compression. IEEE Trans. Circuits Syst. Video Technol. 31(12), 4603\u20134616 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1948_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, W., Yin, Z., Sheng, Z., Li, Y., Ouyang, W., Li, X., Tao, Y., Yang, Z., Cui, B.: Graph attention multi-layer perceptron. In: Proceedings of ACM SIGKDD International Conference Knowledge Discovery Data Mining (KDD), pp. 4560\u20134570 (2022)","DOI":"10.1145\/3534678.3539121"},{"key":"1948_CR28","doi-asserted-by":"crossref","unstructured":"Xiao, A., Yang, X., Lu, S., Guo, Y., Zhang, X.: FPS-Net: a convolutional fusion network for large-scale LiDAR point cloud segmentation. ISPRS J. Photogrammetry Remote Sens. 176 (2021)","DOI":"10.1016\/j.isprsjprs.2021.04.011"},{"key":"1948_CR29","doi-asserted-by":"crossref","unstructured":"Lou, M., Yu, Y.: OverLoCK: an overview-first-look-closely-next ConvNet with context-mixing dynamic kernels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2025)","DOI":"10.1109\/CVPR52734.2025.00021"},{"key":"1948_CR30","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization, arXiv preprint arXiv:1607.06450 (2016)"},{"key":"1948_CR31","unstructured":"Ding, X., et al.: UniRepLKNet: a universal perception large-kernel ConvNet. In: Proceedings of IEEE\/CVF CVPR (2024)"},{"key":"1948_CR32","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G., Albanie, S.: Squeeze-and-excitation networks. In: Proceedings of IEEE CVPR (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1948_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, S., Chen, D., Pan, J., et al.: Adapt or Perish: adaptive sparse transformer with attentive feature refinement for image restoration. In: Proceedings of IEEE\/CVF CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.00285"},{"key":"1948_CR34","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Yu, W.: Research on ResNet34 improved model. In: Proceedings of 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), IEEE (2024)","DOI":"10.1109\/ICIIBMS62405.2024.10792749"},{"key":"1948_CR35","doi-asserted-by":"crossref","unstructured":"Lai, X., Chen, Y., Lu, F., Liu, J., Jia, J.: Spherical transformer for LiDAR-based 3D recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52729.2023.01683"},{"key":"1948_CR36","doi-asserted-by":"crossref","unstructured":"Kong, L., Liu, Y., Chen, R., et al. Rethinking range view representation for LiDAR segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 228\u2013240 (2023)","DOI":"10.1109\/ICCV51070.2023.00028"},{"key":"1948_CR37","doi-asserted-by":"crossref","unstructured":"Ando, A., Gidaris, S., Bursuc, A., et al.: RangeViT: towards vision transformers for 3D semantic segmentation in autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5240\u20135250 (2023)","DOI":"10.1109\/CVPR52729.2023.00507"},{"key":"1948_CR38","doi-asserted-by":"crossref","unstructured":"Sun, L., Dong, J., Tang, J. et al.: Spatially-adaptive feature modulation for efficient image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2023)","DOI":"10.1109\/ICCV51070.2023.01213"},{"key":"1948_CR39","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian Error Linear Units, arXiv preprint arXiv:1606.08415 (2016)"},{"key":"1948_CR40","doi-asserted-by":"crossref","unstructured":"Yang, B., Condurache, A.P.: FLARES: fast and accurate LiDAR multi-range semantic segmentation, arXiv preprint arXiv:2502.09274 (2025)","DOI":"10.1109\/WACV61042.2026.00337"},{"key":"1948_CR41","doi-asserted-by":"crossref","unstructured":"Fong, W.K., Mohan, R., Hurtado, J.V., Zhou, L., Caesar, H., Beijbom, O., Valada, A.: Panoptic nuScenes: a large-scale benchmark for LiDAR panoptic segmentation and tracking. IEEE Robot. Autom. Lett., 7(2) (2022)","DOI":"10.1109\/LRA.2022.3148457"},{"key":"1948_CR42","doi-asserted-by":"crossref","unstructured":"Xiao, A., Huang, J., Xuan, W., et al.: 3D semantic segmentation in the wild: learning generalized models for adverse-condition point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.00905"},{"key":"1948_CR43","unstructured":"Contributors, M.: MMDetection3D: OpenMMLab next-generation platform for general 3D object detection. GitHub repository (2020)"},{"key":"1948_CR44","unstructured":"Loshchilov, I., Hutter, F.: Decoupled Weight Decay Regularization, arXiv preprint arXiv:1711.05101 (2017)"},{"key":"1948_CR45","doi-asserted-by":"crossref","unstructured":"Smith, L.N., Topin, N.: Super-convergence: very fast training of neural networks using large learning rates. In: Proceedings of Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications (2019)","DOI":"10.1117\/12.2520589"},{"issue":"9","key":"1948_CR46","doi-asserted-by":"publisher","first-page":"2697","DOI":"10.3390\/s25092697","volume":"25","author":"C Chen","year":"2025","unstructured":"Chen, C., Zhao, L., Guo, W., et al.: FARVNet: a fast and accurate range-view-based method for semantic segmentation of point clouds. Sensors 25(9), 2697 (2025)","journal-title":"Sensors"},{"key":"1948_CR47","doi-asserted-by":"crossref","unstructured":"Reichert, H., Serfling, B., Sch\u00fcssler, E., et al.: Real-time semantic segmentation of high-resolution automotive LiDAR scans, arXiv preprint arXiv:2504.21602 (2025)","DOI":"10.1109\/ITSC60802.2025.11423654"},{"key":"1948_CR48","doi-asserted-by":"crossref","unstructured":"Cortinhal, T., Tzelepis, G., Aksoy, E.E.: SalsaNext: fast, uncertainty-aware semantic segmentation of LiDAR point clouds. In: International Symposium on Visual Computing, Springer, Berlin, pp. 207\u2013222 (2020)","DOI":"10.1007\/978-3-030-64559-5_16"},{"key":"1948_CR49","unstructured":"Ding, B.: LENet: lightweight and efficient LiDAR semantic segmentation using multi-scale convolution attention, arXiv preprint arXiv:2301.04275 (2023)"},{"key":"1948_CR50","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1948_CR51","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Zhou, Y., Han, J., Ding, G., Sun, J.: Scaling up your kernels to 31$$\\times $$31: revisiting large kernel design in CNNs. In: Proceedings of IEEE\/CVF Conference Computer Vision Pattern Recognition (CVPR), pp. 11963\u201311975 (2022)","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"1948_CR52","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1948_CR53","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., et al.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: Proceedings of IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4213\u20134220 (2019)","DOI":"10.1109\/IROS40897.2019.8967762"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01948-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-026-01948-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01948-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:17:56Z","timestamp":1787156276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-026-01948-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["1948"],"URL":"https:\/\/doi.org\/10.1007\/s11554-026-01948-3","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"3 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"152"}}