{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:51:13Z","timestamp":1762368673222,"version":"build-2065373602"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004192","name":"Jilin Province Science and Technology Development Plan Project","doi-asserted-by":"publisher","award":["20240302096GX"],"award-info":[{"award-number":["20240302096GX"]}],"id":[{"id":"10.13039\/501100004192","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"Joint Fund for Regional Innovation and Development of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20445"],"award-info":[{"award-number":["U21A20445"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jilin Provincial Key Laboratory of Intelligent Sensing and Network Technology","award":["20230508035RC","YDZJ202102CXJD018"],"award-info":[{"award-number":["20230508035RC","YDZJ202102CXJD018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,4,15]]},"DOI":"10.1109\/jiot.2024.3512598","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T14:04:08Z","timestamp":1733753048000},"page":"10183-10193","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid-Fusion Mamba for Multitask Point Cloud Learning With Visual Perception Sensors"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9554-8233","authenticated-orcid":false,"given":"Hongwei","family":"Dong","sequence":"first","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0036-4424","authenticated-orcid":false,"given":"Shun","family":"Na","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5694-4057","authenticated-orcid":false,"given":"Fengye","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3342555"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3376974"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3329236"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3424936"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3170429"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3292374"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3287799"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3310406"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3329884"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3416949"},{"key":"ref11","first-page":"5105","article-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst.","volume":"30","author":"Qi"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref13","first-page":"23192","article-title":"PointNeXT: Revisiting pointnet++ with improved training and scaling strategies","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","volume":"35","author":"Qian"},{"key":"ref14","first-page":"1","article-title":"Rethinking network design and local geometry in point cloud: A simple residual MLP framework","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ma"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20086-1_38"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20086-1_35"},{"key":"ref18","first-page":"27061","article-title":"Point-M2AE: Multi-scale masked autoencoders for hierarchical point cloud pre-training","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","volume":"35","author":"Zhang"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02164"},{"key":"ref20","first-page":"1","article-title":"PointGPT: Auto-regressively generative pre-training from point clouds","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","volume":"36","author":"Chen"},{"key":"ref21","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023","journal-title":"arXiv:2312.00752"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672044"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3430985"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tmm.2023.3314973"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01302"},{"key":"ref26","first-page":"28223","article-title":"Contrast with reconstruct: Contrastive 3D representation learning guided by generative pretraining","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Qi"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28501"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01393"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i6.28380"},{"key":"ref30","first-page":"33330","article-title":"Point transformer v2: Grouped vector attention and partition-based pooling","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","volume":"35","author":"Wu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00463"},{"key":"ref32","first-page":"1","article-title":"Efficiently modeling long sequences with structured state spaces","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Gu"},{"key":"ref33","first-page":"7616","article-title":"It\u2019s raw! Audio generation with state-space models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Goel"},{"key":"ref34","first-page":"2846","article-title":"S4ND: Modeling images and videos as multidimensional signals with state spaces","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","volume":"35","author":"Nguyen"},{"key":"ref35","article-title":"Vision Mamba: Efficient visual representation learning with bidirectional state space model","author":"Zhu","year":"2024","journal-title":"arXiv:2401.09417"},{"key":"ref36","article-title":"Point could Mamba: Point cloud learning via state space model","author":"Zhang","year":"2024","journal-title":"arXiv:2403.00762"},{"key":"ref37","article-title":"PointMamba: A simple state space model for point cloud analysis","author":"Liang","year":"2024","journal-title":"arXiv:2402.10739"},{"key":"ref38","first-page":"652","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Qi"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980238"},{"key":"ref40","first-page":"828","article-title":"PointCNN: Convolution on X-transformed points","volume-title":"Proc. 32nd Conf. Neural Inf. Process. Syst.","volume":"31","author":"Li"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3072214"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00007"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01837"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01327"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-021-0229-5"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3592111"},{"key":"ref47","first-page":"1","article-title":"Autoencoders as cross-modal teachers: Can pretrained 2D image transformers help 3D representation learning?","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Dong"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00516"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00964"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/10957755\/10783777.pdf?arnumber=10783777","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:41:44Z","timestamp":1762368104000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10783777\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":49,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2024.3512598","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"type":"electronic","value":"2327-4662"},{"type":"electronic","value":"2372-2541"}],"subject":[],"published":{"date-parts":[[2025,4,15]]}}}