{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:55:04Z","timestamp":1771959304423,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T00:00:00Z","timestamp":1713657600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T00:00:00Z","timestamp":1713657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0125400"],"award-info":[{"award-number":["2022YFE0125400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0125400"],"award-info":[{"award-number":["2022YFE0125400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0125400"],"award-info":[{"award-number":["2022YFE0125400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0125400"],"award-info":[{"award-number":["2022YFE0125400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0125400"],"award-info":[{"award-number":["2022YFE0125400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0125400"],"award-info":[{"award-number":["2022YFE0125400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Project for Technology Innovation and Application Development of Chongqing Science & Technology Commission","award":["CSTB2022TIAD-STX0006"],"award-info":[{"award-number":["CSTB2022TIAD-STX0006"]}]},{"name":"Major Project for Technology Innovation and Application Development of Chongqing Science & Technology Commission","award":["CSTB2022TIAD-STX0006"],"award-info":[{"award-number":["CSTB2022TIAD-STX0006"]}]},{"name":"Major Project for Technology Innovation and Application Development of Chongqing Science & Technology Commission","award":["CSTB2022TIAD-STX0006"],"award-info":[{"award-number":["CSTB2022TIAD-STX0006"]}]},{"name":"Major Project for Technology Innovation and Application Development of Chongqing Science & Technology Commission","award":["CSTB2022TIAD-STX0006"],"award-info":[{"award-number":["CSTB2022TIAD-STX0006"]}]},{"name":"Major Project for Technology Innovation and Application Development of Chongqing Science & Technology Commission","award":["CSTB2022TIAD-STX0006"],"award-info":[{"award-number":["CSTB2022TIAD-STX0006"]}]},{"name":"Major Project for Technology Innovation and Application Development of Chongqing Science & Technology Commission","award":["CSTB2022TIAD-STX0006"],"award-info":[{"award-number":["CSTB2022TIAD-STX0006"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00138-024-01538-y","type":"journal-article","created":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T07:01:35Z","timestamp":1713682895000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["ET-PointPillars: improved PointPillars for 3D object detection based on optimized voxel downsampling"],"prefix":"10.1007","volume":"35","author":[{"given":"Yiyi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhengyi","family":"Yang","sequence":"additional","affiliation":[]},{"given":"JianLin","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Jiajia","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jiongcheng","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Lihang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wangxin","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,21]]},"reference":[{"key":"1538_CR1","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, X., Song, Z., Bi, J., Zhang, G., Wei, H., Tang, L., Yang, L., Li, J., Jia, C., et al.: Multi-modal 3d object detection in autonomous driving: A survey and taxonomy. IEEE Trans. Intell. Veh. (2023)","DOI":"10.1109\/TIV.2023.3264658"},{"key":"1538_CR2","doi-asserted-by":"crossref","unstructured":"Song, Z., Liu, L., Jia, F., Luo, Y., Zhang, G., Yang, L., Wang, L., Jia, C.: Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook (2024)","DOI":"10.1109\/TITS.2024.3439557"},{"key":"1538_CR3","unstructured":"Zhang, X., Wang, L., Chen, J., Fang, C., Yang, L., Song, Z., Yang, G., Wang, Y., Zhang, X., Li, J., Li, Z., Yang, Q., Zhang, Z., Ge, S.S.: Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving (2023)"},{"key":"1538_CR4","doi-asserted-by":"crossref","unstructured":"Kim, D., Min, J., Song, Y., Kim, C., Ahn, J.: Intelligent risk-identification algorithm with vision and 3d lidar patterns at damaged buildings. Int. Autom. Soft Comput. 36(2) (2023)","DOI":"10.32604\/iasc.2023.034394"},{"key":"1538_CR5","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s10015-020-00617-3","volume":"26","author":"X Wang","year":"2021","unstructured":"Wang, X., Mizukami, Y., Tada, M., Matsuno, F.: Navigation of a mobile robot in a dynamic environment using a point cloud map. Artif. Life Robot. 26, 10\u201320 (2021)","journal-title":"Artif. Life Robot."},{"issue":"118","key":"1538_CR6","first-page":"37","volume":"29","author":"AY Noori","year":"2023","unstructured":"Noori, A.Y.: The preprocessing operation for 3d indoor and outdoor dataset. J. College Basic Educ. 29(118), 37\u201352 (2023)","journal-title":"J. College Basic Educ."},{"key":"1538_CR7","doi-asserted-by":"publisher","unstructured":"Xu, Y., Tong, X., Stilla, U.: Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Auto. Construct 126, 103675 (2021) https:\/\/doi.org\/10.1016\/j.autcon.2021.103675","DOI":"10.1016\/j.autcon.2021.103675"},{"key":"1538_CR8","volume":"118","author":"H Aljumaily","year":"2023","unstructured":"Aljumaily, H., Laefer, D.F., Cuadra, D., Velasco, M.: Point cloud voxel classification of aerial urban lidar using voxel attributes and random forest approach. Int. J. Appl. Earth Obs. Geoinf. 118, 103208 (2023)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"1538_CR9","doi-asserted-by":"crossref","unstructured":"Koide, K., Yokozuka, M., Oishi, S., Banno, A.: Voxelized gicp for fast and accurate 3d point cloud registration. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 11054\u201311059 (2021). IEEE","DOI":"10.1109\/ICRA48506.2021.9560835"},{"key":"1538_CR10","doi-asserted-by":"crossref","unstructured":"Meng, H.-Y., Gao, L., Lai, Y.-K., Manocha, D.: Vv-net: Voxel vae net with group convolutions for point cloud segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8500\u20138508 (2019)","DOI":"10.1109\/ICCV.2019.00859"},{"issue":"23","key":"1538_CR11","doi-asserted-by":"publisher","first-page":"2727","DOI":"10.3390\/rs11232727","volume":"11","author":"M Huang","year":"2019","unstructured":"Huang, M., Wei, P., Liu, X.: An efficient encoding voxel-based segmentation (evbs) algorithm based on fast adjacent voxel search for point cloud plane segmentation. Remote Sensing 11(23), 2727 (2019)","journal-title":"Remote Sensing"},{"key":"1538_CR12","doi-asserted-by":"crossref","unstructured":"Mao, J., Xue, Y., Niu, M., Bai, H., Feng, J., Liang, X., Xu, H., Xu, C.: Voxel transformer for 3d object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3164\u20133173 (2021)","DOI":"10.1109\/ICCV48922.2021.00315"},{"key":"1538_CR13","doi-asserted-by":"crossref","unstructured":"Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., Li, H.: Voxel r-cnn: Towards high performance voxel-based 3d object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1201\u20131209 (2021)","DOI":"10.1609\/aaai.v35i2.16207"},{"key":"1538_CR14","doi-asserted-by":"crossref","unstructured":"Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., Li, H.: Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"1538_CR15","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 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4490\u20134499 (2018)","DOI":"10.1109\/CVPR.2018.00472"},{"key":"1538_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, G., Xie, J., Liu, L., Wang, Z., Yang, K., Song, Z.: Urformer: Unified representation lidar-camera 3d object detection with transformer. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 401\u2013413 (2023). Springer","DOI":"10.1007\/978-981-99-8435-0_32"},{"key":"1538_CR17","doi-asserted-by":"crossref","unstructured":"Song, Z., Zhang, G., Xie, J., Liu, L., Jia, C., Xu, S., Wang, Z.: Voxelnextfusion: A simple, unified and effective voxel fusion framework for multi-modal 3d object detection. arXiv preprint arXiv:2401.02702 (2024)","DOI":"10.1109\/TGRS.2023.3331893"},{"key":"1538_CR18","doi-asserted-by":"crossref","unstructured":"Song, Z., Wei, H., Bai, L., Yang, L., Jia, C.: Graphalign: Enhancing accurate feature alignment by graph matching for multi-modal 3d object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 3358\u20133369 (2023)","DOI":"10.1109\/ICCV51070.2023.00311"},{"key":"1538_CR19","doi-asserted-by":"crossref","unstructured":"Song, Z., Jia, C., Yang, L., Wei, H., Liu, L.: Graphalign++: an accurate feature alignment by graph matching for multi-modal 3d object detection. IEEE Trans. Circ. Syst. Video Technol. (2023)","DOI":"10.1109\/ICCV51070.2023.00311"},{"key":"1538_CR20","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"1538_CR21","doi-asserted-by":"crossref","unstructured":"Shrout, O., Ben-Shabat, Y., Tal, A.: Gravos: Voxel selection for 3d point-cloud detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 21684\u201321693 (2023)","DOI":"10.1109\/CVPR52729.2023.02077"},{"key":"1538_CR22","unstructured":"Zhu, B., Jiang, Z., Zhou, X., Li, Z., Yu, G.: Class-balanced grouping and sampling for point cloud 3d object detection. arXiv:1908.09492 (2019)"},{"key":"1538_CR23","doi-asserted-by":"crossref","unstructured":"Song, Z., Wei, H., Jia, C., Xia, Y., Li, X., Zhang, C.: Vp-net: Voxels as points for 3d object detection. IEEE Trans. Geosci. Remote Sensing (2023)","DOI":"10.1109\/TGRS.2023.3271020"},{"key":"1538_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110080","volume":"259","author":"L Wang","year":"2023","unstructured":"Wang, L., Song, Z., Zhang, X., Wang, C., Zhang, G., Zhu, L., Li, J., Liu, H.: Sat-gcn: Self-attention graph convolutional network-based 3d object detection for autonomous driving. Knowl.-Based Syst. 259, 110080 (2023)","journal-title":"Knowl.-Based Syst."},{"issue":"11","key":"1538_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-3006-9","volume":"63","author":"Y Deng","year":"2020","unstructured":"Deng, Y.: Uncertainty measure in evidence theory. Sci. China Inf. Sci. 63(11), 210201 (2020)","journal-title":"Sci. China Inf. Sci."},{"issue":"4","key":"1538_CR26","doi-asserted-by":"publisher","first-page":"155014771984129","DOI":"10.1177\/1550147719841295","volume":"15","author":"Y Song","year":"2019","unstructured":"Song, Y., Deng, Y.: A new method to measure the divergence in evidential sensor data fusion. Int. J. Distrib. Sens. Netw. 15(4), 1550147719841295 (2019)","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"1538_CR27","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.ins.2019.11.022","volume":"514","author":"F Xiao","year":"2020","unstructured":"Xiao, F.: A new divergence measure for belief functions in d-s evidence theory for multisensor data fusion. Inf. Sci. 514, 462\u2013483 (2020)","journal-title":"Inf. Sci."},{"key":"1538_CR28","doi-asserted-by":"publisher","unstructured":"Huang, M., Liu, Z., Tao, Y.: Mechanical fault diagnosis and prediction in iot based on multi-source sensing data fusion. Simulation Modelling Practice and Theory 102, 101981 (2020) https:\/\/doi.org\/10.1016\/j.simpat.2019.101981 . Special Issue on IoT, Cloud, Big Data and AI in Interdisciplinary Domains","DOI":"10.1016\/j.simpat.2019.101981"},{"issue":"1","key":"1538_CR29","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TFUZZ.2020.3002431","volume":"29","author":"F Xiao","year":"2020","unstructured":"Xiao, F., Cao, Z., Jolfaei, A.: A novel conflict measurement in decision-making and its application in fault diagnosis. IEEE Trans. Fuzzy Syst. 29(1), 186\u2013197 (2020)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1538_CR30","doi-asserted-by":"crossref","unstructured":"Lin, K., Li, Y., Sun, J., Zhou, D., Zhang, Q.: Multi-sensor fusion for body sensor network in medical human-robot interaction scenario. Inf. Fus. 57, 15\u201326 (2020)","DOI":"10.1016\/j.inffus.2019.11.001"},{"issue":"4","key":"1538_CR31","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.3390\/s20041158","volume":"20","author":"G Li","year":"2020","unstructured":"Li, G., Liu, Z., Cai, L., Yan, J.: Standing-posture recognition in human-robot collaboration based on deep learning and the dempster-shafer evidence theory. Sensors 20(4), 1158 (2020)","journal-title":"Sensors"},{"key":"1538_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104442","volume":"141","author":"T Meyer","year":"2022","unstructured":"Meyer, T., Brunn, A., Stilla, U.: Change detection for indoor construction progress monitoring based on bim, point clouds and uncertainties. Autom. Constr. 141, 104442 (2022)","journal-title":"Autom. Constr."},{"issue":"19","key":"1538_CR33","doi-asserted-by":"publisher","first-page":"4116","DOI":"10.3390\/s19194116","volume":"19","author":"K Jo","year":"2019","unstructured":"Jo, K., Lee, S., Kim, C., Sunwoo, M.: Rapid motion segmentation of lidar point cloud based on a combination of probabilistic and evidential approaches for intelligent vehicles. Sensors 19(19), 4116 (2019)","journal-title":"Sensors"},{"key":"1538_CR34","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.04.011","volume":"107","author":"W Xiao","year":"2015","unstructured":"Xiao, W., Vallet, B., Br\u00e9dif, M., Paparoditis, N.: Street environment change detection from mobile laser scanning point clouds. ISPRS J. Photogramm. Remote. Sens. 107, 38\u201349 (2015)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"1538_CR35","doi-asserted-by":"publisher","unstructured":"Wu, Q., Zhou, M., Hu, B.: Object detection based on fusing monocular camera and lidar data in decision level using d-s evidence theory. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 476\u2013481 (2020). https:\/\/doi.org\/10.1109\/CASE48305.2020.9216767","DOI":"10.1109\/CASE48305.2020.9216767"},{"key":"1538_CR36","doi-asserted-by":"crossref","unstructured":"Kanimozhi, U., Manjula, D.: An intelligent incremental filtering feature selection and clustering algorithm for effective classification. Intell. Autom. Soft Comput., 1\u20139 (2017)","DOI":"10.1080\/10798587.2017.1307626"},{"issue":"7","key":"1538_CR37","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1002\/int.21956","volume":"33","author":"H Zheng","year":"2018","unstructured":"Zheng, H., Deng, Y.: Evaluation method based on fuzzy relations between dempster-shafer belief structure. Int. J. Intell. Syst. 33(7), 1343\u20131363 (2018)","journal-title":"Int. J. Intell. Syst."},{"key":"1538_CR38","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.isprsjprs.2019.12.009","volume":"160","author":"H Rastiveis","year":"2020","unstructured":"Rastiveis, H., Shams, A., Sarasua, W.A., Li, J.: Automated extraction of lane markings from mobile lidar point clouds based on fuzzy inference. ISPRS J. Photogramm. Remote. Sens. 160, 149\u2013166 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"1538_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2023.170642","volume":"276","author":"C Zhang","year":"2023","unstructured":"Zhang, C., Zhou, H., Chen, B., Peng, Y., Duan, J.: Hybrid simplification algorithm for unorganized point cloud based on two-level fuzzy decision making. Optik 276, 170642 (2023)","journal-title":"Optik"},{"issue":"12","key":"1538_CR40","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.1109\/TFUZZ.2020.2992611","volume":"28","author":"M Zhong","year":"2020","unstructured":"Zhong, M., Li, C., Liu, L., Wen, J., Ma, J., Yu, X.: Fuzzy neighborhood learning for deep 3-d segmentation of point cloud. IEEE Trans. Fuzzy Syst. 28(12), 3181\u20133192 (2020)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1538_CR41","doi-asserted-by":"publisher","unstructured":"Nguyen, T., Yoo, M.: Fusing lidar sensor and rgb camera for object detection in autonomous vehicle with fuzzy logic approach. In: 2021 International Conference on Information Networking (ICOIN), 788\u2013791 (2021). https:\/\/doi.org\/10.1109\/ICOIN50884.2021.9334015","DOI":"10.1109\/ICOIN50884.2021.9334015"},{"key":"1538_CR42","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, X., Zhao, F., Wu, C., Wang, Y., Song, Z., Yang, L., Li, J., Liu, H.: Fuzzy-nms: Improving 3d object detection with fuzzy classification in nms. arXiv preprint arXiv:2310.13951 (2023)","DOI":"10.1109\/TIV.2024.3409684"},{"key":"1538_CR43","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Proc. Syst. 28 (2015)"},{"key":"1538_CR44","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1538_CR45","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, 21\u201337 (2016). Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1538_CR46","doi-asserted-by":"crossref","unstructured":"Paigwar, A., Sierra-Gonzalez, D., Erkent, \u00d6., Laugier, C.: Frustum-pointpillars: A multi-stage approach for 3d object detection using rgb camera and lidar. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2926\u20132933 (2021)","DOI":"10.1109\/ICCVW54120.2021.00327"},{"key":"1538_CR47","doi-asserted-by":"crossref","unstructured":"McCrae, S., Zakhor, A.: 3d object detection for autonomous driving using temporal lidar data. In: 2020 IEEE International Conference on Image Processing (ICIP), 2661\u20132665 (2020). IEEE","DOI":"10.1109\/ICIP40778.2020.9191134"},{"key":"1538_CR48","doi-asserted-by":"crossref","unstructured":"Li, X., Liang, B., Huang, J., Peng, Y., Yan, Y., Li, J., Shang, W., Wei, W., et al.: Pillar-based 3d object detection from point cloud with multiattention mechanism. Wirel. Commun. Mob. Comput. 2023 (2023)","DOI":"10.1155\/2023\/5603123"},{"key":"1538_CR49","doi-asserted-by":"publisher","unstructured":"Wei, Z., Wang, F., Fan, J., Gao, B.: An efficient point cloud-based 3d single stage object detector. In: 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), 1\u20135 (2021). https:\/\/doi.org\/10.1109\/CVCI54083.2021.9661200","DOI":"10.1109\/CVCI54083.2021.9661200"},{"key":"1538_CR50","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.inffus.2018.04.003","volume":"46","author":"F Xiao","year":"2019","unstructured":"Xiao, F.: Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Inf. Fus. 46, 23\u201332 (2019)","journal-title":"Inf. Fus."}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01538-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01538-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01538-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T18:44:48Z","timestamp":1731782688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-024-01538-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,21]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["1538"],"URL":"https:\/\/doi.org\/10.1007\/s00138-024-01538-y","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,21]]},"assertion":[{"value":"27 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2024","order":4,"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 that they have no conflicts of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"56"}}