{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:59:11Z","timestamp":1774447151944,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Natural Science Foundation of Anhui Province","award":["2208085MF173"],"award-info":[{"award-number":["2208085MF173"]}]},{"name":"the Financial Support of the Key Research and Development Projects of Anhui","award":["202104a05020003"],"award-info":[{"award-number":["202104a05020003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the wave of research on autonomous driving, 3D object detection from the Bird\u2019s Eye View (BEV) perspective has emerged as a pivotal area of focus. The essence of this challenge is the effective fusion of camera and LiDAR data into the BEV. Current approaches predominantly train and predict within the front view and Cartesian coordinate system, often overlooking the inherent structural and operational differences between cameras and LiDAR sensors. This paper introduces CL-FusionBEV, an innovative 3D object detection methodology tailored for sensor data fusion in the BEV perspective. Our approach initiates with a view transformation, facilitated by an implicit learning module that transitions the camera\u2019s perspective to the BEV space, thereby aligning the prediction module. Subsequently, to achieve modal fusion within the BEV framework, we employ voxelization to convert the LiDAR point cloud into BEV space, thereby generating LiDAR BEV spatial features. Moreover, to integrate the BEV spatial features from both camera and LiDAR, we have developed a multi-modal cross-attention mechanism and an implicit multi-modal fusion network, designed to enhance the synergy and application of dual-modal data. To counteract potential deficiencies in global reasoning and feature interaction arising from multi-modal cross-attention, we propose a BEV self-attention mechanism that facilitates comprehensive global feature operations. Our methodology has undergone rigorous evaluation on a substantial dataset within the autonomous driving domain, the nuScenes dataset. The outcomes demonstrate that our method achieves a mean Average Precision (mAP) of 73.3% and a nuScenes Detection Score (NDS) of 75.5%, particularly excelling in the detection of cars and pedestrians with high accuracies of 89% and 90.7%, respectively. Additionally, CL-FusionBEV exhibits superior performance in identifying occluded and distant objects, surpassing existing comparative methods.<\/jats:p>","DOI":"10.1007\/s40747-024-01567-0","type":"journal-article","created":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T07:02:41Z","timestamp":1722063761000},"page":"7681-7696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["CL-fusionBEV: 3D object detection method with camera-LiDAR fusion in Bird\u2019s Eye View"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1533-8154","authenticated-orcid":false,"given":"Peicheng","family":"Shi","sequence":"first","affiliation":[]},{"given":"Zhiqiang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xinlong","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Aixi","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"issue":"3","key":"1567_CR1","first-page":"1075","volume":"65","author":"C Yan","year":"2017","unstructured":"Yan C, Salman E (2017) Mono3D: open source cell library for monolithic 3-D integrated circuits. IEEE Trans Circuits Syst Video Technol 65(3):1075\u20131085","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"5","key":"1567_CR2","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1109\/TPAMI.2017.2706685","volume":"40","author":"X Chen","year":"2017","unstructured":"Chen X, Kundu K, Zhu Y et al (2017) 3d object proposals using stereo imagery for accurate object class detection. IEEE Trans Pattern Anal Mach Intell 40(5):1259\u20131272","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1567_CR3","first-page":"110","volume":"53","author":"C Pham","year":"2017","unstructured":"Pham C, Jeon JW (2017) Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks. Signal Process: Image Commun 53:110\u2013122","journal-title":"Signal Process: Image Commun"},{"key":"1567_CR4","doi-asserted-by":"crossref","unstructured":"Xu B, Chen Z (2018) Multi-level fusion based 3d object detection from monocular images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2345\u20132353.","DOI":"10.1109\/CVPR.2018.00249"},{"key":"1567_CR5","doi-asserted-by":"publisher","first-page":"16373","DOI":"10.1007\/s00500-023-09164-y","volume":"27","author":"H Dou","year":"2023","unstructured":"Dou H, Liu Y, Chen S, Bilal H et al (2023) A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways. Soft Comput 27:16373\u201316388","journal-title":"Soft Comput"},{"key":"1567_CR6","doi-asserted-by":"crossref","unstructured":"Zhou Y, Tuzel O. Voxelnet (2018) 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.","DOI":"10.1109\/CVPR.2018.00472"},{"issue":"10","key":"1567_CR7","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 (2018) Second: sparsely embedded convolutional detection. Sensors 18(10):3337","journal-title":"Sensors"},{"key":"1567_CR8","doi-asserted-by":"crossref","unstructured":"Yin T, Zhou X, Krahenbuhl P (2021) Center-based 3d object detection and tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 11784\u201311793.","DOI":"10.1109\/CVPR46437.2021.01161"},{"issue":"8","key":"1567_CR9","first-page":"2647","volume":"43","author":"S Shi","year":"2020","unstructured":"Shi S, Wang Z, Shi J et al (2020) From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network. IEEE Trans Pattern Anal Mach Intell 43(8):2647\u20132664","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1567_CR10","doi-asserted-by":"crossref","unstructured":"Lang A H, Vora S, Caesar H, et al. (2019) Pointpillars: Fast encoders for object detection from point clouds. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 12697\u201312705.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"1567_CR11","unstructured":"Qi C R, Su H, Mo K, et al. (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 652\u2013660."},{"key":"1567_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","volume":"140","author":"C Zhang","year":"2018","unstructured":"Zhang C, Pan X, Li H et al (2018) A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS J Photogramm Remote Sens 140:133\u2013144","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1567_CR13","unstructured":"Qi C R, Yi L, Su H, et al. (2017) Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv Neural Inf Process Syst. p 30."},{"key":"1567_CR14","doi-asserted-by":"crossref","unstructured":"Bilal H, Yao W, Guo Y, Wu Y, Guo J (2017) \u201cExperimental validation of fuzzy PID control of flexible joint system in presence of uncertainties\u201d 2017 36th Chinese Control Conference (CCC), Dalian, China. pp. 4192-4197","DOI":"10.23919\/ChiCC.2017.8028015"},{"key":"1567_CR15","doi-asserted-by":"crossref","unstructured":"Behley J, Garbade M, Milioto A, et al. (2019) Semantickitti: a dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 9297\u20139307.","DOI":"10.1109\/ICCV.2019.00939"},{"key":"1567_CR16","doi-asserted-by":"crossref","unstructured":"Liu Z, Tang H, Amini A, et al. (2023) Bevfusion: multi-task multi-sensor fusion with unified bird\u2019s-eye view representation. In: 2023 IEEE international conference on robotics and automation (ICRA). IEEE. pp 2774-2781","DOI":"10.1109\/ICRA48891.2023.10160968"},{"key":"1567_CR17","doi-asserted-by":"crossref","unstructured":"Chen X, Ma H, Wan J, et al. (2017) Multi-view 3d object detection network for autonomous driving. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp 1907\u20131915.","DOI":"10.1109\/CVPR.2017.691"},{"key":"1567_CR18","doi-asserted-by":"publisher","first-page":"18195","DOI":"10.1007\/s00500-023-09278-3","volume":"27","author":"Q Wu","year":"2023","unstructured":"Wu Q, Li X, Wang K, Bilal H et al (2023) Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles. Soft Comput 27:18195\u201318213","journal-title":"Soft Comput"},{"key":"1567_CR19","doi-asserted-by":"crossref","unstructured":"Caesar H, Bankiti V, Lang A H, et al. (2020) nuscenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 11621\u201311631.","DOI":"10.1109\/CVPR42600.2020.01164"},{"issue":"1","key":"1567_CR20","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/TCSVT.2017.2772892","volume":"29","author":"H Yan","year":"2017","unstructured":"Yan H, Yu X, Zhang Y, Zhang S, Zhao X, Zhang L (2017) Single image depth estimation with normal guided scale invariant deep convolutional fields. IEEE Trans Circuits Syst Video Technol 29(1):80\u201392","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1567_CR21","doi-asserted-by":"publisher","first-page":"4987","DOI":"10.1007\/s00500-023-08026-x","volume":"27","author":"H Bilal","year":"2023","unstructured":"Bilal H, Yin B, Aslam MS et al (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27:4987\u20135001","journal-title":"Soft Comput"},{"key":"1567_CR22","unstructured":"Xie E, Yu Z, Zhou D, et al. M $^ 2$ (2022) BEV: multi-camera joint 3D detection and segmentation with unified birds-eye view representation. arXiv preprint arXiv:2204.05088. Accessed 1 Dec 2023"},{"key":"1567_CR23","doi-asserted-by":"crossref","unstructured":"Philion J, Fidler S (2020) Lift, splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3d, in Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020. Proceedings, Part XIV 16. Springer International Publishing, New York. pp 194-210","DOI":"10.1007\/978-3-030-58568-6_12"},{"key":"1567_CR24","unstructured":"Huang J, Huang G, Zhu Z, et al. (2021) Bevdet: high-performance multi-camera 3d object detection in bird-eye-view, arXiv preprint arXiv:2112.11790. Accessed 29 Nov 2023"},{"key":"1567_CR25","doi-asserted-by":"publisher","first-page":"4029","DOI":"10.1007\/s00500-023-07923-5","volume":"27","author":"H Bilal","year":"2023","unstructured":"Bilal H, Yin B, Kumar A et al (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27:4029\u20134039","journal-title":"Soft Comput"},{"key":"1567_CR26","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang T, Zhang X, et al. (2022) Petr: position embedding transformation for multi-view 3d object detection. In: European Conference on Computer Vision. Springer Nature, Cham. pp 531\u2013548.","DOI":"10.1007\/978-3-031-19812-0_31"},{"key":"1567_CR27","unstructured":"Wang Y, Guizilini V C, Zhang T, et al. (2022) Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In: Conference on Robot Learning. PMLR. 180\u2013191."},{"key":"1567_CR28","doi-asserted-by":"crossref","unstructured":"Li Z, Wang W, Li H, et al. (2022) Bevformer: learning bird\u2019s-eye-view representation from multi-camera images via spatiotemporal transformers. In: European conference on computer vision. Springer Nature, Cham. pp 1\u201318.","DOI":"10.1007\/978-3-031-20077-9_1"},{"key":"1567_CR29","doi-asserted-by":"crossref","unstructured":"Yang B, Luo W, Urtasun R (2018) Pixor: real-time 3d object detection from point clouds. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp 7652\u20137660.","DOI":"10.1109\/CVPR.2018.00798"},{"key":"1567_CR30","doi-asserted-by":"crossref","unstructured":"Shi S, Wang X, Li H (2019) Pointrcnn: 3d object proposal generation and detection from point cloud, in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 70\u2013779.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"1567_CR31","doi-asserted-by":"crossref","unstructured":"Yang Z, Sun Y, Liu S, et al. (2019) Std: Sparse-to-dense 3d object detector for point cloud. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 1951\u20131960.","DOI":"10.1109\/ICCV.2019.00204"},{"key":"1567_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3268849","author":"B Fan","year":"2024","unstructured":"Fan B, Zhang K, Tian J (2024) HCPVF: hierarchical cascaded point-voxel fusion for 3D object detection. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2023.3268849","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1567_CR33","doi-asserted-by":"crossref","unstructured":"Qi C R, Liu W, Wu C, et al. (2018) Frustum pointnets for 3d object detection from rgb-d data. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 918\u2013927.","DOI":"10.1109\/CVPR.2018.00102"},{"key":"1567_CR34","doi-asserted-by":"crossref","unstructured":"Shin K, Kwon YP, Tomizuka M. Roarnet (2019) A robust 3d object detection based on region approximation refinement. In: 2019 IEEE intelligent vehicles symposium (IV). IEEE. pp 2510-2515","DOI":"10.1109\/IVS.2019.8813895"},{"key":"1567_CR35","doi-asserted-by":"crossref","unstructured":"Wang Z, Jia K (2019) Frustum convnet: sliding frustums to aggregate local point-wise features for amodal 3d object detection in 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. pp 1742\u20131749.","DOI":"10.1109\/IROS40897.2019.8968513"},{"key":"1567_CR36","doi-asserted-by":"crossref","unstructured":"Xu D, Anguelov D, Jain A (2018) Pointfusion: deep sensor fusion for 3d bounding box estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 244\u2013253.","DOI":"10.1109\/CVPR.2018.00033"},{"key":"1567_CR37","first-page":"10421","volume":"35","author":"T Liang","year":"2022","unstructured":"Liang T, Xie H, Yu K et al (2022) Bevfusion: a simple and robust lidar-camera fusion framework. Adv Neural Inf Process Syst 35:10421\u201310434","journal-title":"Adv Neural Inf Process Syst"},{"key":"1567_CR38","unstructured":"Cai H, Zhang Z, Zhou Z, et al. (2023) BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird\u2019s-Eye-View via Cross-Modality Guidance and Temporal Aggregation, arXiv preprint arXiv:2303.17099. Accessed 3 Dec 2023"},{"key":"1567_CR39","doi-asserted-by":"crossref","unstructured":"Bai X, Hu Z, Zhu X, et al. (2022) Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 1090\u20131099.","DOI":"10.1109\/CVPR52688.2022.00116"},{"key":"1567_CR40","doi-asserted-by":"crossref","unstructured":"Wang H, Tang H, Shi S, et al. (2023) UniTR: a unified and efficient multi-modal transformer for Bird\u2019s-Eye-View Representation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 6792\u20136802.","DOI":"10.1109\/ICCV51070.2023.00625"},{"key":"1567_CR41","first-page":"1992","volume":"35","author":"Z Yang","year":"2022","unstructured":"Yang Z, Chen J, Miao Z et al (2022) Deepinteraction: 3d object detection via modality interaction. Adv Neural Inf Process Syst 35:1992\u20132005","journal-title":"Adv Neural Inf Process Syst"},{"key":"1567_CR42","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al. (2016) Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1567_CR43","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. (2016) SSD: single shot multibox detector. In Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14. Springer International Publishing. pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1567_CR44","doi-asserted-by":"crossref","unstructured":"Lin T Y, Goyal P, Girshick R, et al. (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"1567_CR45","unstructured":"Contributors M (2020) MMDetection3D: OpenMMLab next-generation platform for general 3D object detection."},{"key":"1567_CR46","unstructured":"Paszke A, Gross S, Massa F, et al. (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. p 32."},{"key":"1567_CR47","doi-asserted-by":"crossref","unstructured":"Sun P, Kretzschmar H, Dotiwalla X, et al. (2020) Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 2446\u20132454.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"1567_CR48","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition. IEEE. pp 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"1567_CR49","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. Accessed 2 Jan 2024"},{"key":"1567_CR50","doi-asserted-by":"crossref","unstructured":"Chen X, Zhang T, Wang Y, et al. (2023) Futr3d: a unified sensor fusion framework for 3d detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 172\u2013181.","DOI":"10.1109\/CVPRW59228.2023.00022"},{"key":"1567_CR51","doi-asserted-by":"crossref","unstructured":"Chen Z, Li Z, Zhang S, et al. (2022) Autoalignv2: deformable feature aggregation for dynamic multi-modal 3d object detection. arXiv preprint arXiv:2207.10316. Accessed 5 Dec 2023","DOI":"10.1007\/978-3-031-20074-8_36"},{"key":"1567_CR52","unstructured":"Zhang Y, Zhu Z, Zheng W, et al. (2022) Beverse: unified perception and prediction in birds-eye-view for vision-centric autonomous driving. arXiv preprint arXiv:2205.09743. Accessed 7 Dec 2023"},{"issue":"2","key":"1567_CR53","first-page":"1486","volume":"37","author":"Y Li","year":"2023","unstructured":"Li Y, Bao H, Ge Z et al (2023) Bevstereo: enhancing depth estimation in multi-view 3d object detection with temporal stereo. Proc AAAI Conf Artif Intell 37(2):1486\u20131494","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1567_CR54","unstructured":"Zhu B, Jiang Z, Zhou X, et al. (2019) Class-balanced grouping and sampling for point cloud 3d object detection. arXiv preprint arXiv:1908.09492. Accessed 4 Dec 2023"},{"key":"1567_CR55","first-page":"21224","volume":"33","author":"Q Chen","year":"2020","unstructured":"Chen Q, Sun L, Cheung E et al (2020) Every view counts: cross-view consistency in 3d object detection with hybrid-cylindrical-spherical voxelization. Adv Neural Inf Process Syst 33:21224\u201321235","journal-title":"Adv Neural Inf Process Syst"},{"key":"1567_CR56","doi-asserted-by":"crossref","unstructured":"Chen Q, Sun L, Wang Z, et al. (2020) Object as hotspots: an anchor-free 3d object detection approach via firing of hotspots. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXI 16. Springer International Publishing, Cham. pp 68\u201384","DOI":"10.1007\/978-3-030-58589-1_5"},{"key":"1567_CR57","doi-asserted-by":"crossref","unstructured":"Vora S, Lang AH, Helou B, et al. (2020) Pointpainting: sequential fusion for 3d object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 4604\u20134612.","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"1567_CR58","doi-asserted-by":"crossref","unstructured":"Yoo J H, Kim Y, Kim J, et al. (2020) 3d-cvf: generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection. In: Computer vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXVII 16. Springer International Publishing, Cham. pp 720\u2013736","DOI":"10.1007\/978-3-030-58583-9_43"},{"key":"1567_CR59","first-page":"16494","volume":"34","author":"T Yin","year":"2021","unstructured":"Yin T, Zhou X, Kr\u00e4henb\u00fchl P (2021) Multimodal virtual point 3d detection. Adv Neural Inf Process Syst 34:16494\u201316507","journal-title":"Adv Neural Inf Process Syst"},{"key":"1567_CR60","doi-asserted-by":"crossref","unstructured":"Wang C, Ma C, Zhu M, et al. (2021) PointAugmenting: cross-modal augmentation for 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 11794\u201311803.","DOI":"10.1109\/CVPR46437.2021.01162"},{"key":"1567_CR61","doi-asserted-by":"crossref","unstructured":"Xu S, Zhou D, Fang J, et al. (2021) Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE. pp 3047\u20133054","DOI":"10.1109\/ITSC48978.2021.9564951"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01567-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01567-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01567-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T22:11:03Z","timestamp":1729116663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01567-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,27]]},"references-count":61,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1567"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01567-0","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,27]]},"assertion":[{"value":"21 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2024","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}