{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:25:12Z","timestamp":1740101112273,"version":"3.37.3"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,9]]},"DOI":"10.1109\/smc53654.2022.9945072","type":"proceedings-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T20:49:04Z","timestamp":1668804544000},"page":"2421-2426","source":"Crossref","is-referenced-by-count":0,"title":["Multi-stream Feature Aggregation Network for 3D Object Detection in Point Cloud"],"prefix":"10.1109","author":[{"given":"Yingjie","family":"Hou","sequence":"first","affiliation":[{"name":"Qingdao University,School of Computer Science and Technology,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Qingdao University,School of Computer Science and Technology,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Structure information is the key: Self-attention roi feature extractor in 3d object detection","author":"zhang","year":"2021","journal-title":"arXiv preprint arXiv 2111 02269"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1109\/TCSVT.2021.3100848"},{"key":"ref12","first-page":"923","article-title":"End-to-end multi-view fusion for 3d object detection in lidar point clouds","author":"zhou","year":"2020","journal-title":"Conference on Robot Learning"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1609\/aaai.v36i1.19897"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/ICCV48922.2021.00315"},{"key":"ref15","first-page":"10526","article-title":"Pvrcnn: Point-voxel feature set abstraction for 3d object detection","author":"shi","year":"2020","journal-title":"CVPR"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/CVPR.2019.01298"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/CVPR42600.2020.01105"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1016\/j.compbiomed.2021.104836"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1109\/ICCV.2017.324"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/ICCV48922.2021.00272"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/CVPR46437.2021.00738"},{"key":"ref27","first-page":"2647","article-title":"From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network","volume":"43","author":"shi","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1109\/CVPR.2019.00086"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.3390\/s18103337"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/CVPR.2018.00472"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/ICCV48922.2021.00274"},{"key":"ref7","article-title":"Voxel r-cnn: Towards high performance voxel-based 3d object detection","author":"deng","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref2","article-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"qi","year":"2017","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1145\/3474085.3475314"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1109\/CVPR.2017.16"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1007\/978-3-030-01264-9_48"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/CVPR.2012.6248074"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/CVPR.2019.00111"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/CVPR.2017.691"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1109\/CVPR.2019.01298"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1007\/978-3-030-58583-9_43"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/CVPR.2018.00102"}],"event":{"name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","start":{"date-parts":[[2022,10,9]]},"location":"Prague, Czech Republic","end":{"date-parts":[[2022,10,12]]}},"container-title":["2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9945068\/9945069\/09945072.pdf?arnumber=9945072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:55:14Z","timestamp":1670874914000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9945072\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,9]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/smc53654.2022.9945072","relation":{},"subject":[],"published":{"date-parts":[[2022,10,9]]}}}