{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T14:49:36Z","timestamp":1782571776149,"version":"3.54.5"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005315","name":"Natural Science Foundation of Ningbo Municipality","doi-asserted-by":"publisher","award":["2023J285"],"award-info":[{"award-number":["2023J285"]}],"id":[{"id":"10.13039\/501100005315","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s00371-026-04559-y","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T14:35:02Z","timestamp":1782570902000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3D object detection using a multi-scale point transformer-RCNN"],"prefix":"10.1007","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6450-1715","authenticated-orcid":false,"given":"Malik","family":"Haris","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5108-517X","authenticated-orcid":false,"given":"Guoqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-0179","authenticated-orcid":false,"given":"Liming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Shahid","family":"Mastoi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asif","family":"Raza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"4559_CR1","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1049\/iet-its.2018.5576","volume":"13","author":"N Qi","year":"2019","unstructured":"Qi, N., Yang, X., Li, C., Lu, R., He, C., Cao, L.: Unstructured road detection via combining the model-based and feature-based methods. IET Intell. Transp. Syst. 13, 1533\u20131544 (2019)","journal-title":"IET Intell. Transp. Syst."},{"key":"4559_CR2","first-page":"1932","volume":"10","author":"M Haris","year":"2021","unstructured":"Haris, M., Glowacz, A.: Road object detection: a comparative study of deep learning-based algorithms. Electronics (Switzerland) 10, 1932 (2021)","journal-title":"Electronics (Switzerland)"},{"key":"4559_CR3","doi-asserted-by":"crossref","unstructured":"Dai, J.; He, K.; Sun, J. Instance-Aware Semantic Segmentation via Multi-Task Network Cascades. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition; 2016; pp. 3150\u20133158.","DOI":"10.1109\/CVPR.2016.343"},{"key":"4559_CR4","doi-asserted-by":"crossref","unstructured":"He, K.; Gkioxari, G.; Doll\u00e1r, P.; Girshick, R. Mask R-CNN. In Proceedings of the IEEE international conference on computer vision; IEEE Venice, Italy, 2017.","DOI":"10.1109\/ICCV.2017.322"},{"key":"4559_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20174719","volume":"20","author":"M Haris","year":"2020","unstructured":"Haris, M., Hou, J.: Obstacle detection and safely navigate the autonomous vehicle from unexpected obstacles on the driving lane. Sensors (Switzerland) 20, 1\u201322 (2020). https:\/\/doi.org\/10.3390\/s20174719","journal-title":"Sensors (Switzerland)"},{"key":"4559_CR6","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s00371-021-02353-6","volume":"39","author":"M Haris","year":"2023","unstructured":"Haris, M., Hou, J., Wang, X.: Lane line detection and departure estimation in a complex environment by using an asymmetric kernel convolution algorithm. Vis. Comput. 39, 519\u2013538 (2023)","journal-title":"Vis. Comput."},{"key":"4559_CR7","doi-asserted-by":"publisher","first-page":"3782","DOI":"10.1109\/TITS.2019.2892405","volume":"20","author":"E Arnold","year":"2019","unstructured":"Arnold, E., Al-Jarrah, O.Y., Dianati, M., Fallah, S., Oxtoby, D., Mouzakitis, A.: A survey on 3d object detection methods for autonomous driving applications. IEEE Trans. Intell. Transp. Syst. 20, 3782\u20133795 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4559_CR8","first-page":"513","volume":"40","author":"ZY Cai","year":"2020","unstructured":"Cai, Z.Y., Jin, C.Q.: Object contour recognition based on 2D Lidar point cloud. Applied Laser 40, 513\u2013518 (2020)","journal-title":"Applied Laser"},{"key":"4559_CR9","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43, 4338\u20134364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4559_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03480-6","author":"H Wang","year":"2024","unstructured":"Wang, H., Chen, X., Yuan, Q., Liu, P.: A review of 3D object detection based on autonomous driving. Vis. Comput. (2024). https:\/\/doi.org\/10.1007\/s00371-024-03480-6","journal-title":"Vis. Comput."},{"key":"4559_CR11","doi-asserted-by":"crossref","unstructured":"Qi, C.R.; Liu, W.; Wu, C.; Su, H.; Guibas, L.J. Frustum Pointnets for 3d Object Detection from Rgb-d Data. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition; 2018; pp. 918\u2013927.","DOI":"10.1109\/CVPR.2018.00102"},{"key":"4559_CR12","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 the Proceedings of the IEEE conference on computer vision and pattern recognition; 2017; pp. 652\u2013660."},{"key":"4559_CR13","unstructured":"Qi, C.R.; Yi, L.; Su, H.; Guibas, L.J. Pointnet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Adv. Neural Inf. Process. Syst. 2017, 30."},{"key":"4559_CR14","doi-asserted-by":"crossref","unstructured":"Shi, S.; Wang, X.; Li, H. Pointrcnn: 3d Object Proposal Generation and Detection from Point Cloud. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2019; pp. 770\u2013779.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"4559_CR15","first-page":"2647","volume":"43","author":"S Shi","year":"2020","unstructured":"Shi, S., Wang, Z., Shi, J., Wang, X., Li, H.: From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network. IEEE Trans. Pattern Anal. Mach. Intell. 43, 2647\u20132664 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4559_CR16","doi-asserted-by":"publisher","first-page":"3434","DOI":"10.1109\/LRA.2018.2852843","volume":"3","author":"Y Zeng","year":"2018","unstructured":"Zeng, Y., Hu, Y., Liu, S., Ye, J., Han, Y., Li, X., Sun, N.: Rt3d: real-time 3-d vehicle detection in Lidar point cloud for autonomous driving. IEEE Robot. Autom. Lett. 3, 3434\u20133440 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"4559_CR17","unstructured":"Liang, Z.; Zhang, M.; Zhang, Z.; Zhao, X.; Pu, S. Rangercnn: Towards Fast and Accurate 3d Object Detection with Range Image Representation. arXiv preprint arXiv:2009.002062020."},{"key":"4559_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, Y.; Tuzel, O. Voxelnet: End-to-End Learning for Point Cloud Based 3d Object Detection. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition; 2018; pp. 4490\u20134499.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"4559_CR19","unstructured":"Han, K.; Wang, Y.; Chen, H.; Chen, X.; Guo, J.; Liu, Z.; Tang, Y.; Xiao, A.; Xu, C.; Xu, Y. A Survey on Visual Transformer. arXiv e-prints 2020, arXiv-2012."},{"key":"4559_CR20","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1007\/s00371-025-04245-5","volume":"42","author":"X Chen","year":"2026","unstructured":"Chen, X., Zhang, J., Ding, C., Cao, R., Zhu, L., Hu, W., Zang, Y., Chen, T.: DeepSketch2Wear: democratizing 3D garment creation via freehand sketches and text. Vis. Comput. 42, 130 (2026)","journal-title":"Vis. Comput."},{"key":"4559_CR21","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s00371-025-04201-3","volume":"42","author":"Q Yang","year":"2026","unstructured":"Yang, Q., Bai, Y., Liu, F., Li, Y.: Application of the fast diffusion model ST-Diffusion in multi-modal lip-to-speech generation. Vis. Comput. 42, 81 (2026)","journal-title":"Vis. Comput."},{"key":"4559_CR22","unstructured":"Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S. An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv:2010.119292020."},{"key":"4559_CR23","doi-asserted-by":"crossref","unstructured":"Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; Zagoruyko, S. End-to-End Object Detection with Transformers. In Proceedings of the European conference on computer vision; Springer, 2020; pp. 213\u2013229.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"4559_CR24","unstructured":"Zheng, M.; Gao, P.; Zhang, R.; Li, K.; Wang, X.; Li, H.; Dong, H. End-to-End Object Detection with Adaptive Clustering Transformer. arXiv preprint arXiv:2011.093152020."},{"key":"4559_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y.; Xu, Z.; Wang, X.; Shen, C.; Cheng, B.; Shen, H.; Xia, H. End-to-End Video Instance Segmentation with Transformers. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2021; pp. 8741\u20138750.","DOI":"10.1109\/CVPR46437.2021.00863"},{"key":"4559_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, H.; Jiang, L.; Jia, J.; Torr, P.H.S.; Koltun, V. Point Transformer. In Proceedings of the Proceedings of the IEEE\/CVF International Conference on Computer Vision; 2021; pp. 16259\u201316268.","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"4559_CR27","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"M-H Guo","year":"2021","unstructured":"Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R.R., Hu, S.-M.: Pct: point cloud transformer. Comput. Vis. Media 7, 187\u2013199 (2021)","journal-title":"Comput. Vis. Media"},{"key":"4559_CR28","doi-asserted-by":"crossref","unstructured":"Pan, X.; Xia, Z.; Song, S.; Li, L.E.; Huang, G. 3d Object Detection with Pointformer. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2021; pp. 7463\u20137472.","DOI":"10.1109\/CVPR46437.2021.00738"},{"key":"4559_CR29","doi-asserted-by":"crossref","unstructured":"Guo, G.; Wang, H.; Bell, D.; Bi, Y.; Greer, K. KNN Model-Based Approach in Classification. In Proceedings of the OTM Confederated International Conferences\" On the Move to Meaningful Internet Systems\"; Springer, 2003; pp. 986\u2013996.","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"4559_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2026.106016","author":"M Haris","year":"2026","unstructured":"Haris, M., Zhang, Y., Zhang, G., Mastoi, M.S., Hassan, M., Raza, A., Li, Z.: Innovative approaches in image-based 3D object detection for autonomous driving: a comprehensive review. Digit. Signal Process. (2026). https:\/\/doi.org\/10.1016\/j.dsp.2026.106016","journal-title":"Digit. Signal Process."},{"key":"4559_CR31","doi-asserted-by":"crossref","unstructured":"Kumar, A.; Brazil, G.; Liu, X. Groomed-Nms: Grouped Mathematically Differentiable Nms for Monocular 3d Object Detection. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2021; pp. 8973\u20138983.","DOI":"10.1109\/CVPR46437.2021.00886"},{"key":"4559_CR32","doi-asserted-by":"crossref","unstructured":"Yang, Z.; Sun, Y.; Liu, S.; Shen, X.; Jia, J. Std: Sparse-to-Dense 3d Object Detector for Point Cloud. In Proceedings of the Proceedings of the IEEE\/CVF international conference on computer vision; 2019; pp. 1951\u20131960.","DOI":"10.1109\/ICCV.2019.00204"},{"key":"4559_CR33","first-page":"1","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph Cnn for learning on point clouds. ACM Trans. Graph. 38, 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"4559_CR34","doi-asserted-by":"publisher","first-page":"2769","DOI":"10.1007\/s00521-020-05150-9","volume":"33","author":"M Ju","year":"2021","unstructured":"Ju, M., Luo, J., Wang, Z., Luo, H.: Adaptive feature fusion with attention mechanism for multi-scale target detection. Neural Comput. Appl. 33, 2769\u20132781 (2021)","journal-title":"Neural Comput. Appl."},{"key":"4559_CR35","unstructured":"Bruna, J.; Zaremba, W.; Szlam, A.; LeCun, Y. Spectral Networks and Locally Connected Networks on Graphs. arXiv preprint arXiv:1312.62032013."},{"key":"4559_CR36","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2016","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset [J]. Int. J. Robot. Res. 32, 1231\u20131237 (2016)","journal-title":"Int. J. Robot. Res."},{"key":"4559_CR37","doi-asserted-by":"crossref","unstructured":"Caesar, H.; Bankiti, V.; Lang, A.H.; Vora, S.; Liong, V.E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; Beijbom, O. Nuscenes: A Multimodal Dataset for Autonomous Driving. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020; pp. 11621\u201311631.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"4559_CR38","doi-asserted-by":"crossref","unstructured":"Geiger, A.; Lenz, P.; Urtasun, R. Are We Ready for Autonomous Driving? The Kitti Vision Benchmark Suite. In Proceedings of the 2012 IEEE conference on computer vision and pattern recognition; IEEE, 2012; pp. 3354\u20133361.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"4559_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3525772","author":"H Pan","year":"2025","unstructured":"Pan, H., Jia, Y., Wang, J., Sun, W.: MonoAMNet: Three-stage real-time monocular 3D object detection with adaptive methods. IEEE Trans. Intell. Transp. Syst. (2025). https:\/\/doi.org\/10.1109\/TITS.2025.3525772","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4559_CR40","doi-asserted-by":"crossref","unstructured":"Meier, J.; G\u00fcnther, F.; Marin, R.; Dhaouadi, O.; Kaiser, J.; Cremers, D. IDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection. In Proceedings of the Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision; 2026; pp. 181\u2013191.","DOI":"10.1109\/WACV61042.2026.00026"},{"key":"4559_CR41","unstructured":"Zhang, Z.; Kumar, A.; Ganesan, G.C.; Liu, X. Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection. arXiv preprint arXiv:2505.045942025."},{"key":"4559_CR42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2025.3577618","author":"H Yao","year":"2025","unstructured":"Yao, H., Han, P., Chen, J., Wang, Z., Qiu, Y., Wang, X., Chai, X., Cao, C., Jin, W.: MonOri: Orientation-guided PnP for monocular 3-D object detection. IEEE Trans. Neural Netw. Learn. Syst. (2025). https:\/\/doi.org\/10.1109\/TNNLS.2025.3577618","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"4559_CR43","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3568418","author":"W Ye","year":"2025","unstructured":"Ye, W., Xia, Q., Wu, H., Dong, Z., Zhong, R., Wang, C., Wen, C.: Fade3d: Fast and deployable 3d object detection for autonomous driving. IEEE Trans. Intell. Transp. Syst. (2025). https:\/\/doi.org\/10.1109\/TITS.2025.3568418","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4559_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2025.3636670","volume":"10","author":"T Prasanth","year":"2025","unstructured":"Prasanth, T., Padhy, R.P., Sivaselvan, B.: LiDAR sensor-based dual-scale fusion approach for bird\u2019s-eye view sensing in autonomous vehicles. IEEE Sens. Lett. 10, 1\u20134 (2025)","journal-title":"IEEE Sens. Lett."},{"key":"4559_CR45","doi-asserted-by":"crossref","unstructured":"Guo, X.; Shi, S.; Wang, X.; Li, H. Liga-Stereo: Learning Lidar Geometry Aware Representations for Stereo-Based 3d Detector. In Proceedings of the Proceedings of the IEEE\/CVF international conference on computer vision; 2021; pp. 3153\u20133163.","DOI":"10.1109\/ICCV48922.2021.00314"},{"key":"4559_CR46","doi-asserted-by":"crossref","unstructured":"Ku, J.; Mozifian, M.; Lee, J.; Harakeh, A.; Waslander, S.L. Joint 3d Proposal Generation and Object Detection from View Aggregation. In Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS); IEEE, 2018; pp. 1\u20138.","DOI":"10.1109\/IROS.2018.8594049"},{"key":"4559_CR47","unstructured":"Prasanth, T.; Padhy, R.P.; Sivaselvan, B. M3DNet: Minimalist 3D Detection Backbone for Efficient and Accurate 3D Object Detection."},{"key":"4559_CR48","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.: Second: sparsely embedded convolutional detection. Sensors 18, 3337 (2018)","journal-title":"Sensors"},{"key":"4559_CR49","doi-asserted-by":"crossref","unstructured":"Prasanth, T.; Padhy, R.P.; Sivaselvan, B. PCNet3D++: A Pillar-Based Cascaded 3D Object Detection Model with an Enhanced 2D Backbone. Image Vis. Comput. 2025, 105854.","DOI":"10.1016\/j.imavis.2025.105854"},{"key":"4559_CR50","doi-asserted-by":"crossref","unstructured":"Prasanth, T.; Padhy, R.P.; Sivaselvan, B. MSFASA-3DNet: Multi-Scale Feature Aggregation and Spatial Attention for 3D Object Detection. IEEE Transactions on Artificial Intelligence 2026.","DOI":"10.1109\/TAI.2026.3674873"},{"key":"4559_CR51","doi-asserted-by":"crossref","unstructured":"Lang, A.H.; Vora, S.; Caesar, H.; Zhou, L.; Yang, J.; Beijbom, O. Pointpillars: Fast Encoders for Object Detection from Point Clouds. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2019; pp. 12697\u201312705.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"4559_CR52","doi-asserted-by":"publisher","first-page":"10421","DOI":"10.52202\/068431-0757","volume":"35","author":"T Liang","year":"2022","unstructured":"Liang, T., Xie, H., Yu, K., Xia, Z., Lin, Z., Wang, Y., Tang, T., Wang, B., Tang, Z.: Bevfusion: a simple and robust lidar-camera fusion framework. Adv. Neural. Inf. Process. Syst. 35, 10421\u201310434 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4559_CR53","doi-asserted-by":"publisher","first-page":"11677","DOI":"10.1609\/aaai.v34i07.6837","volume":"34","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Zhao, X., Huang, T., Hu, R., Zhou, Y., Bai, X.: Tanet: Robust 3d Object Detection from Point Clouds with Triple Attention. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence 34, 11677\u201311684 (2020)","journal-title":"In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4559_CR54","unstructured":"He, Q.; Wang, Z.; Zeng, H.; Zeng, Y.; Liu, S.; Zeng, B. SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. In Proceedings of the AAAI Conference on Artificial Intelligence; 2020."},{"key":"4559_CR55","doi-asserted-by":"crossref","unstructured":"Wang, Z.; Jia, K. Frustum Convnet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3d Object Detection. In Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS); IEEE, 2019; pp. 1742\u20131749.","DOI":"10.1109\/IROS40897.2019.8968513"},{"key":"4559_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104594","volume":"129","author":"Q Tang","year":"2023","unstructured":"Tang, Q., Bai, X., Guo, J., Pan, B., Jiang, W.: DFAF3D: a dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image Vis. Comput. 129, 104594 (2023)","journal-title":"Image Vis. Comput."},{"key":"4559_CR57","doi-asserted-by":"crossref","unstructured":"Shi, W.; Rajkumar, R. Point-Gnn: Graph Neural Network for 3d Object Detection in a Point Cloud. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020; pp. 1711\u20131719.","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"4559_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.115820","volume":"242","author":"J Cao","year":"2025","unstructured":"Cao, J., Peng, Y., Wei, H., Mo, L., Fan, L., Wang, L.: KPTr: key point transformer for LiDAR-based 3D object detection. Measurement 242, 115820 (2025)","journal-title":"Measurement"},{"key":"4559_CR59","unstructured":"Mo, Y.; Wu, Y.; Zhao, J.; Wang, J.; Hu, Y.; Yan, J. Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection. arXiv preprint arXiv:2507.138992025."},{"key":"4559_CR60","doi-asserted-by":"crossref","unstructured":"Yang, Z.; Sun, Y.; Liu, S.; Jia, J. 3dssd: Point-Based 3d Single Stage Object Detector. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020; pp. 11040\u201311048.","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"4559_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104155","volume":"62","author":"Q Zheng","year":"2025","unstructured":"Zheng, Q., Wu, S., Wei, J.: VoxT-GNN: a 3D object detection approach from point cloud based on voxel-level transformer and graph neural network. Inf. Process. Manag. 62, 104155 (2025)","journal-title":"Inf. Process. Manag."},{"key":"4559_CR62","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1007\/s00371-024-03481-5","volume":"41","author":"L Sun","year":"2025","unstructured":"Sun, L., Li, Y., Qin, W.: PEPillar: a point-enhanced pillar network for efficient 3D object detection in autonomous driving. Vis. Comput. 41, 1777\u20131788 (2025)","journal-title":"Vis. Comput."},{"key":"4559_CR63","doi-asserted-by":"publisher","first-page":"5036","DOI":"10.1109\/TCSVT.2023.3248656","volume":"33","author":"W Xiao","year":"2023","unstructured":"Xiao, W., Peng, Y., Liu, C., Gao, J., Wu, Y., Li, X.: Balanced sample assignment and objective for single-model multi-class 3D object detection. IEEE Trans. Circuits Syst. Video Technol. 33, 5036\u20135048 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4559_CR64","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1609\/aaai.v37i2.25233","volume":"37","author":"Y Li","year":"2023","unstructured":"Li, Y., Ge, Z., Yu, G., Yang, J., Wang, Z., Shi, Y., Sun, J., Li, Z.: Bevdepth: Acquisition of Reliable Depth for Multi-View 3d Object Detection. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence 37, 1477\u20131485 (2023)","journal-title":"In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4559_CR65","doi-asserted-by":"crossref","unstructured":"Liu, Y.; Yan, J.; Jia, F.; Li, S.; Gao, A.; Wang, T.; Zhang, X. Petrv2: A Unified Framework for 3d Perception from Multi-Camera Images. In Proceedings of the Proceedings of the IEEE\/CVF international conference on computer vision; 2023; pp. 3262\u20133272.","DOI":"10.1109\/ICCV51070.2023.00302"},{"key":"4559_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, Y.; Chen, J.; Huang, D. Cat-Det: Contrastively Augmented Transformer for Multi-Modal 3d Object Detection. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2022; pp. 908\u2013917.","DOI":"10.1109\/CVPR52688.2022.00098"},{"key":"4559_CR67","doi-asserted-by":"crossref","unstructured":"Liu, Y.; Wang, T.; Zhang, X.; Sun, J. Petr: Position Embedding Transformation for Multi-View 3d Object Detection. In Proceedings of the European conference on computer vision; Springer, 2022; pp. 531\u2013548.","DOI":"10.1007\/978-3-031-19812-0_31"},{"key":"4559_CR68","unstructured":"Huang, J.; Huang, G. Bevdet4d: Exploit Temporal Cues in Multi-Camera 3d Object Detection. arXiv preprint arXiv:2203.170542023."},{"key":"4559_CR69","unstructured":"Huang, J.; Huang, G.; Zhu, Z.; Ye, Y.; Du, D. Bevdet: High-Performance Multi-Camera 3d Object Detection in Bird-Eye-View. arXiv preprint arXiv:2112.117902021."},{"key":"4559_CR70","doi-asserted-by":"crossref","unstructured":"Yang, C.; Chen, Y.; Tian, H.; Tao, C.; Zhu, X.; Zhang, Z.; Huang, G.; Li, H.; Qiao, Y.; Lu, L. Bevformer v2: Adapting Modern Image Backbones to Bird\u2019s-Eye-View Recognition via Perspective Supervision. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2023; pp. 17830\u201317839.","DOI":"10.1109\/CVPR52729.2023.01710"},{"key":"4559_CR71","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3555229","author":"S Chen","year":"2025","unstructured":"Chen, S., Zhang, H., Zheng, N.: Leveraging anchor-based LiDAR 3D object detection via point assisted sample selection. IEEE Trans. Intell. Transp. Syst. (2025). https:\/\/doi.org\/10.1109\/TITS.2025.3555229","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4559_CR72","doi-asserted-by":"crossref","unstructured":"Chen, Y.; Liu, J.; Zhang, X.; Qi, X.; Jia, J. Voxelnext: Fully Sparse Voxelnet for 3d Object Detection and Tracking. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2023; pp. 21674\u201321683.","DOI":"10.1109\/CVPR52729.2023.02076"},{"key":"4559_CR73","first-page":"1","volume":"42","author":"P-S Wang","year":"2023","unstructured":"Wang, P.-S.: Octformer: octree-based transformers for 3d point clouds. ACM Transactions on Graphics (TOG) 42, 1\u201311 (2023)","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"4559_CR74","doi-asserted-by":"crossref","unstructured":"Wang, H.; Shi, C.; Shi, S.; Lei, M.; Wang, S.; He, D.; Schiele, B.; Wang, L. Dsvt: Dynamic Sparse Voxel Transformer with Rotated Sets. In Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2023; pp. 13520\u201313529.","DOI":"10.1109\/CVPR52729.2023.01299"},{"key":"4559_CR75","doi-asserted-by":"crossref","unstructured":"Zhao, H.; Jiang, L.; Fu, C.-W.; Jia, J. Pointweb: Enhancing Local Neighborhood Features for Point Cloud Processing. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2019; pp. 5565\u20135573.","DOI":"10.1109\/CVPR.2019.00571"},{"key":"4559_CR76","doi-asserted-by":"crossref","unstructured":"Bai, X.; Hu, Z.; Zhu, X.; Huang, Q.; Chen, Y.; Fu, H.; Tai, C.-L. Transfusion: Robust Lidar-Camera Fusion for 3d Object Detection with Transformers. In Proceedings of the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2022; pp. 1090\u20131099.","DOI":"10.1109\/CVPR52688.2022.00116"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04559-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04559-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04559-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T14:35:30Z","timestamp":1782570930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04559-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":76,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["4559"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04559-y","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]},"assertion":[{"value":"19 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2026","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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"366"}}