{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T14:36:15Z","timestamp":1730212575342,"version":"3.28.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1109\/cvpr52729.2023.01290","type":"proceedings-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T13:30:52Z","timestamp":1692711052000},"page":"13425-13434","source":"Crossref","is-referenced-by-count":2,"title":["Center Focusing Network for Real-Time LiDAR Panoptic Segmentation"],"prefix":"10.1109","author":[{"given":"Xiaoyan","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology"}]},{"given":"Gang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Mogo Auto Intelligence and Telemetics Information Technology Co. Ltd."}]},{"given":"Boyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology"}]},{"given":"Yongli","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3060405"},{"journal-title":"Sparse cross-scale attention network for efficient lidar panoptic segmentation","year":"2022","author":"xu","key":"ref35"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3148457"},{"key":"ref34","first-page":"1","article-title":"Squeeze-segv3: Spatially-adaptive convolution for efficient point-cloud segmentation","author":"xu","year":"2020","journal-title":"European Conference on Computer Vision"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01289"},{"journal-title":"Lidarmultinet Towards a unified multi-task network for lidar perception","year":"2022","author":"ye","key":"ref37"},{"key":"ref14","first-page":"2961","article-title":"Mask r-cnn","author":"he","year":"2017","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.3390\/s18103337","article-title":"Second: Sparsely embed-ded convolutional detection","volume":"18","author":"yan","year":"2018","journal-title":"SENSORS"},{"journal-title":"IEEE Transactions on Robotics","article-title":"Efficientlps: Efficient lidar panoptic segmentation","year":"2021","author":"sirohi","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref11","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"ester","year":"1996","journal-title":"KDD"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref10","first-page":"207","article-title":"Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds","author":"cortinhal","year":"2020","journal-title":"International Symposium on Visual Computing"},{"key":"ref32","first-page":"685","article-title":"Searching efficient 3d architectures with sparse point-voxel convolution","author":"tang","year":"2020","journal-title":"European Conference on Computer Vision"},{"key":"ref2","first-page":"9297","article-title":"Semantickitti: A dataset for semantic scene understanding of lidar sequences","author":"behley","year":"2019","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00184"},{"journal-title":"MOPT Multi-Object Panoptic Tracking","year":"2020","author":"hurtado","key":"ref17"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01299"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00963"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340837"},{"journal-title":"CPGNet Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation","year":"2022","author":"li","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00847"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967762"},{"journal-title":"Smac-seg Lidar panoptic segmentation via sparse multi-directional attention clustering","year":"2021","author":"li","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01151"},{"journal-title":"Cpseg Cluster-free panoptic segmentation of 3d lidar point cloud","year":"2021","author":"li","key":"ref21"},{"journal-title":"Pointnet++ Deep hierarchical feature learning on point sets in a metric space","year":"2017","author":"qi","key":"ref28"},{"key":"ref27","first-page":"652","article-title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","author":"qi","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01577"},{"key":"ref8","first-page":"3075","article-title":"4d spatio-temporal convnets: Minkowski convolutional neu-ral networks","author":"choy","year":"2019","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01236"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"ref4","first-page":"4413","article-title":"The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks","author":"berman","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561476"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37456-2_14"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00981"}],"event":{"name":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","start":{"date-parts":[[2023,6,17]]},"location":"Vancouver, BC, Canada","end":{"date-parts":[[2023,6,24]]}},"container-title":["2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10203037\/10203050\/10205087.pdf?arnumber=10205087","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T13:56:27Z","timestamp":1694440587000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10205087\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/cvpr52729.2023.01290","relation":{},"subject":[],"published":{"date-parts":[[2023,6]]}}}