{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:07Z","timestamp":1740108067391,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973065","52075531"],"award-info":[{"award-number":["61973065","52075531"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N2104008"],"award-info":[{"award-number":["N2104008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09241-1","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T18:01:44Z","timestamp":1701367304000},"page":"3167-3178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HFE-Net: hierarchical feature extraction and coordinate conversion of point cloud for object 6D pose estimation"],"prefix":"10.1007","volume":"36","author":[{"given":"Ze","family":"Shen","sequence":"first","affiliation":[]},{"given":"Hao","family":"Chu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8296-8039","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Shangdong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"issue":"12","key":"9241_CR1","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/TVCG.2015.2513408","volume":"22","author":"E Marchand","year":"2015","unstructured":"Marchand E, Uchiyama H, Spindler F (2015) Pose estimation for augmented reality: a hands-on survey. IEEE Trans Visual Comput Grap 22(12):2633\u20132651","journal-title":"IEEE Trans Visual Comput Grap"},{"key":"9241_CR2","doi-asserted-by":"crossref","unstructured":"Chen X, Ma H, Wan J, Li B, Xia T (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":"9241_CR3","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":"9241_CR4","doi-asserted-by":"crossref","unstructured":"Zhu M, Derpanis KG, Yang Y, Brahmbhatt S, Zhang M, Phillips C, Lecce M, Daniilidis K (2014) Single image 3D object detection and pose estimation for grasping. In: 2014 IEEE international conference on robotics and automation (ICRA), pp 3936\u20133943. IEEE","DOI":"10.1109\/ICRA.2014.6907430"},{"key":"9241_CR5","unstructured":"Tremblay J, To T, Sundaralingam B, Xiang Y, Fox D, Birchfield S (2018) Deep object pose estimation for semantic robotic grasping of household objects. arXiv preprint arXiv:1809.10790"},{"key":"9241_CR6","doi-asserted-by":"crossref","unstructured":"Rodrigues JJ, Kim J-S, Furukawa M, Xavier J, Aguiar P, Kanade T (2012) 6D pose estimation of textureless shiny objects using random ferns for bin-picking. In: 2012 IEEE\/RSJ international conference on intelligent robots and systems, pp 3334\u20133341. IEEE","DOI":"10.1109\/IROS.2012.6385680"},{"key":"9241_CR7","doi-asserted-by":"crossref","unstructured":"Brachmann E, Krull A, Michel F, Gumhold S, Shotton J, Rother C (2014) Learning 6D object pose estimation using 3d object coordinates. In: European conference on computer vision. Springer, pp 536\u2013551","DOI":"10.1007\/978-3-319-10605-2_35"},{"key":"9241_CR8","doi-asserted-by":"crossref","unstructured":"Hinterstoisser S, Lepetit V, Ilic S, Holzer S, Bradski G, Konolige K, Navab N (2012) Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Asian conference on computer vision. Springer, pp 548\u2013562","DOI":"10.1007\/978-3-642-37331-2_42"},{"key":"9241_CR9","doi-asserted-by":"crossref","unstructured":"Xiang Y, Schmidt T, Narayanan V, Fox D (2017) Posecnn: a convolutional neural network for 6d object pose estimation in cluttered scenes. arXiv preprint arXiv:1711.00199","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"9241_CR10","unstructured":"Do T-T, Cai M, Pham T, Reid I (2018) Deep-6dpose: recovering 6D object pose from a single RGB image. arXiv preprint arXiv:1802.10367"},{"key":"9241_CR11","doi-asserted-by":"crossref","unstructured":"Kehl W, Manhardt F, Tombari F, Ilic S, Navab N (2017) Ssd-6D: Making RGB-based 3D detection and 6D pose estimation great again. In: Proceedings of the IEEE international conference on computer vision, pp 1521\u20131529 (2017)","DOI":"10.1109\/ICCV.2017.169"},{"key":"9241_CR12","doi-asserted-by":"crossref","unstructured":"Wang C, et al (2019) Densefusion: 6D object pose estimation by iterative dense fusion. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3343\u20133352","DOI":"10.1109\/CVPR.2019.00346"},{"key":"9241_CR13","doi-asserted-by":"crossref","unstructured":"Mo N, Gan W, Yokoya N, Chen S (2022) Es6d: a computation efficient and symmetry-aware 6D pose regression framework. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6718\u20136727","DOI":"10.1109\/CVPR52688.2022.00660"},{"key":"9241_CR14","doi-asserted-by":"crossref","unstructured":"Rad M, Lepetit V (2017) Bb8: a scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In: Proceedings of the IEEE international conference on computer vision, pp 3828\u20133836","DOI":"10.1109\/ICCV.2017.413"},{"key":"9241_CR15","doi-asserted-by":"crossref","unstructured":"Hu Y, Hugonot J, Fua P, Salzmann M (2019) Segmentation-driven 6D object pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3385\u20133394","DOI":"10.1109\/CVPR.2019.00350"},{"key":"9241_CR16","doi-asserted-by":"crossref","unstructured":"Peng S, Liu Y, Huang Q, Zhou X, Bao H (2019) Pvnet: pixel-wise voting network for 6dof pose estimation. In: Proceedings of the IEEE\/cvf conference on computer vision and pattern recognition, pp 4561\u20134570","DOI":"10.1109\/CVPR.2019.00469"},{"key":"9241_CR17","doi-asserted-by":"crossref","unstructured":"He Y, Sun W, Huang H, Liu J, Fan H, Sun J (2020) Pvn3d: a deep point-wise 3d keypoints voting network for 6dof pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11632\u201311641","DOI":"10.1109\/CVPR42600.2020.01165"},{"key":"9241_CR18","doi-asserted-by":"crossref","unstructured":"He Y, Huang H, Fan H, Chen Q, Sun J (2021) Ffb6d: a full flow bidirectional fusion network for 6d pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3003\u20133013","DOI":"10.1109\/CVPR46437.2021.00302"},{"key":"9241_CR19","doi-asserted-by":"crossref","unstructured":"Wu Y, Zand M, Etemad A, Greenspan M (2022) Vote from the center: 6 dof pose estimation in rgb-d images by radial keypoint voting. In: European conference on computer vision. Springer, pp 335\u2013352","DOI":"10.1007\/978-3-031-20080-9_20"},{"key":"9241_CR20","doi-asserted-by":"crossref","unstructured":"Xu Z, Zhang Y, Chen K, Jia K (2022) Bico-net: regress globally, match locally for robust 6d pose estimation. arXiv preprint arXiv:2205.03536","DOI":"10.24963\/ijcai.2022\/210"},{"key":"9241_CR21","doi-asserted-by":"crossref","unstructured":"Li H, Lin J, Jia K (2022) Dcl-net: deep correspondence learning network for 6d pose estimation. In: European conference on computer vision. Springer, pp 369\u2013385","DOI":"10.1007\/978-3-031-20077-9_22"},{"key":"9241_CR22","doi-asserted-by":"crossref","unstructured":"Trabelsi A, Chaabane M, Blanchard N, Beveridge R (2021) A pose proposal and refinement network for better 6d object pose estimation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 2382\u20132391","DOI":"10.1109\/WACV48630.2021.00243"},{"key":"9241_CR23","doi-asserted-by":"crossref","unstructured":"Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European conference on computer vision. Springer, pp 404\u2013417","DOI":"10.1007\/11744023_32"},{"key":"9241_CR24","doi-asserted-by":"crossref","unstructured":"Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2, pp 1150\u20131157. IEEE","DOI":"10.1109\/ICCV.1999.790410"},{"issue":"3","key":"9241_CR25","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s11263-005-3674-1","volume":"66","author":"F Rothganger","year":"2006","unstructured":"Rothganger F, Lazebnik S, Schmid C, Ponce J (2006) 3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. Int J Comput Vis 66(3):231\u2013259","journal-title":"Int J Comput Vis"},{"key":"9241_CR26","doi-asserted-by":"crossref","unstructured":"Tekin B, Sinha SN, Fua P (2018) Real-time seamless single shot 6d object pose prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 292\u2013301","DOI":"10.1109\/CVPR.2018.00038"},{"key":"9241_CR27","doi-asserted-by":"crossref","unstructured":"Kendall A, Grimes M, Cipolla R (2015) Posenet: a convolutional network for real-time 6-dof camera relocalization. In: Proceedings of the IEEE international conference on computer vision, pp 2938\u20132946","DOI":"10.1109\/ICCV.2015.336"},{"key":"9241_CR28","doi-asserted-by":"crossref","unstructured":"Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European conference on computer vision. Springer, pp 483\u2013499","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"9241_CR29","doi-asserted-by":"crossref","unstructured":"Oberweger M, Rad M, Lepetit V (2018) Making deep heatmaps robust to partial occlusions for 3d object pose estimation. In: Proceedings of the European conference on computer vision (ECCV), pp 119\u2013134","DOI":"10.1007\/978-3-030-01267-0_8"},{"key":"9241_CR30","unstructured":"Suwajanakorn S, Snavely N, Tompson JJ, Norouzi M (2018) Discovery of latent 3d keypoints via end-to-end geometric reasoning. Adv Neur Inf Process Syst 31"},{"key":"9241_CR31","doi-asserted-by":"crossref","unstructured":"Krull A, Michel F, Brachmann E, Gumhold S, Ihrke S, Rother C (2014) 6-dof model based tracking via object coordinate regression. In: Asian Conference on Computer Vision. Springer, pp. 384\u2013399","DOI":"10.1007\/978-3-319-16817-3_25"},{"issue":"4","key":"9241_CR32","doi-asserted-by":"publisher","first-page":"3960","DOI":"10.1109\/LRA.2018.2858446","volume":"3","author":"A Nigam","year":"2018","unstructured":"Nigam A, Penate-Sanchez A, Agapito L (2018) Detect globally, label locally: learning accurate 6-dof object pose estimation by joint segmentation and coordinate regression. IEEE Robot Autom Lett 3(4):3960\u20133967","journal-title":"IEEE Robot Autom Lett"},{"key":"9241_CR33","doi-asserted-by":"crossref","unstructured":"Brachmann E, Michel F, Krull A, Yang MY, Gumhold S, et al. (2016) Uncertainty-driven 6d pose estimation of objects and scenes from a single RGB image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3364\u20133372","DOI":"10.1109\/CVPR.2016.366"},{"key":"9241_CR34","doi-asserted-by":"crossref","unstructured":"Li Z, Wang G, Ji X (2019) Cdpn: Coordinates-based disentangled pose network for real-time RGB-based 6-dof object pose estimation. In: Proceedings of the IEEE\/cvf international conference on computer vision, pp 7678\u20137687","DOI":"10.1109\/ICCV.2019.00777"},{"key":"9241_CR35","doi-asserted-by":"crossref","unstructured":"Park K, Patten T, Vincze M (2019) Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7668\u20137677","DOI":"10.1109\/ICCV.2019.00776"},{"key":"9241_CR36","doi-asserted-by":"crossref","unstructured":"Zakharov S, Shugurov I, Ilic S (2019) Dpod: 6d pose object detector and refiner. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1941\u20131950","DOI":"10.1109\/ICCV.2019.00203"},{"key":"9241_CR37","doi-asserted-by":"crossref","unstructured":"Hodan T, Barath, D, Matas, J (2020) Epos: estimating 6d pose of objects with symmetries. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11703\u201311712","DOI":"10.1109\/CVPR42600.2020.01172"},{"key":"9241_CR38","doi-asserted-by":"crossref","unstructured":"He K, et al (2017) Mask r-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"9241_CR39","doi-asserted-by":"crossref","unstructured":"Lan S, Yu R, Yu G, Davis LS (2019) Modeling local geometric structure of 3d point clouds using geo-cnn. In: Proceedings of the IEEE\/cvf conference on computer vision and pattern recognition, pp 998\u20131008","DOI":"10.1109\/CVPR.2019.00109"},{"key":"9241_CR40","doi-asserted-by":"crossref","unstructured":"Drost B, Ulrich M, Navab N, Ilic S (2010) Model globally, match locally: efficient and robust 3d object recognition. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 998\u20131005 (2010). IEEE","DOI":"10.1109\/CVPR.2010.5540108"},{"key":"9241_CR41","doi-asserted-by":"crossref","unstructured":"Hinterstoisser S, Holzer S, Cagniart C, Ilic S, Konolige K, Navab N, Lepetit V (2011) Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: 2011 International conference on computer vision, pp 858\u2013865. IEEE","DOI":"10.1109\/ICCV.2011.6126326"},{"key":"9241_CR42","doi-asserted-by":"crossref","unstructured":"Chen W, Jia X, Chang HJ, Duan J, Leonardis A (2020) G2l-net: global to local network for real-time 6d pose estimation with embedding vector features. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4233\u20134242","DOI":"10.1109\/CVPR42600.2020.00429"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09241-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09241-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09241-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:13:57Z","timestamp":1705994037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09241-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9241"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09241-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"6 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2023","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}