{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:08:58Z","timestamp":1761948538034,"version":"3.37.3"},"reference-count":29,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2940411","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T19:51:31Z","timestamp":1568231491000},"page":"127042-127054","source":"Crossref","is-referenced-by-count":7,"title":["PolishNet-2d and PolishNet-3d: Deep Learning-Based Workpiece Recognition"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1593-9497","authenticated-orcid":false,"given":"Fuqiang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00102"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.169"},{"key":"ref13","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298854"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref16","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref17","first-page":"2553","article-title":"Deep neural networks for object detection","author":"szegedy","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"article-title":"OverFeat: Integrated recognition, localization and detection using convolutional networks","year":"0","author":"sermanet","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.276"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SMI.2005.27"},{"key":"ref4","first-page":"1912","article-title":"3D ShapeNets: A deep representation for volumetric shapes","author":"wu","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1080\/16864360.2005.10738325"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref29","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref5","first-page":"307","article-title":"FPNN: Field probing neural networks for 3D data","author":"li","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"article-title":"Hashmod: A hashing method for scalable 3D object detection","year":"0","author":"kehl","key":"ref8"},{"article-title":"RotationNet: Joint learning of object classification and viewpoint estimation using unaligned 3D object dataset","year":"0","author":"kanezaki","key":"ref7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.94"},{"key":"ref9","first-page":"205","article-title":"Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation","author":"kehl","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref1","first-page":"652","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","author":"qi","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"article-title":"Object detectors emerge in deep scene CNNs","year":"0","author":"zhou","key":"ref22"},{"key":"ref21","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00979"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.99"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/781606.781659"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/BF01845691","article-title":"A neural network approach to the classification of 3D prismatic parts","volume":"11","author":"wu","year":"1996","journal-title":"Int J Adv Manuf Technol"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08832125.pdf?arnumber=8832125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T17:20:11Z","timestamp":1643304011000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8832125\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2940411","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}