{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T09:18:18Z","timestamp":1788427098784,"version":"build-2803163510"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"name":"National Key Research &#x0026; Development Program of China","award":["2018YFB1004300"],"award-info":[{"award-number":["2018YFB1004300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61890954"],"award-info":[{"award-number":["61890954"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772462"],"award-info":[{"award-number":["61772462"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572429"],"award-info":[{"award-number":["61572429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472352"],"award-info":[{"award-number":["61472352"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Microsoft Research Asia"},{"name":"NSF","award":["CHS-1845026"],"award-info":[{"award-number":["CHS-1845026"]}]},{"DOI":"10.13039\/100006602","name":"Air Force Research Laboratory","doi-asserted-by":"publisher","award":["FA9453-18-2-0022"],"award-info":[{"award-number":["FA9453-18-2-0022"]}],"id":[{"id":"10.13039\/100006602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Visual. Comput. Graphics"],"published-print":{"date-parts":[[2020,7,1]]},"DOI":"10.1109\/tvcg.2018.2887262","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T14:45:45Z","timestamp":1545144345000},"page":"2403-2416","source":"Crossref","is-referenced-by-count":23,"title":["H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5485-3752","authenticated-orcid":false,"given":"Tianjia","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7645-5931","authenticated-orcid":false,"given":"Yin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlin","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiming","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4243-6112","authenticated-orcid":false,"given":"Kun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACPR.2017.108"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2015.XI.035"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.99"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13343"},{"key":"ref37","article-title":"High performance convolutional neural networks for document processing","author":"chellapilla","year":"2006","journal-title":"Proc Int'l Workshop Frontiers in Handwriting Recognition"},{"key":"ref36","article-title":"Spatially-sparse convolutional neural networks","author":"graham","year":"2014","journal-title":"arXiv 1409 6070"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00961"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989161"},{"key":"ref60","article-title":"Joint 2D-3D-semantic data for indoor scene understanding","author":"armeni","year":"2017","journal-title":"arXiv 1702 01105"},{"key":"ref28","first-page":"77","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":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073616"},{"key":"ref29","first-page":"5105","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","author":"qi","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc 25th Int Conf Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2988458.2988469"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2017.11.010"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2437384"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_14"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2835487"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566589"},{"key":"ref50","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"hinton","year":"2012","journal-title":"arXiv 1207 0580"},{"key":"ref51","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/1618452.1618500"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00532"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7091-6242-2_28"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366154"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980238"},{"key":"ref54","first-page":"89","article-title":"Shrec'16 track: Large-scale 3D shape retrieval from shapenet core55","author":"savva","year":"2016","journal-title":"Proc Eurographics Workshop 3D Object Retrieval"},{"key":"ref53","article-title":"Generative and discriminative voxel modeling with convolutional neural networks","author":"brock","year":"2016","journal-title":"Proceedings of NIPS Workshop on 3D Deep Learning"},{"key":"ref52","first-page":"307","article-title":"FPNN: Field probing neural networks for 3D data","author":"li","year":"2016","journal-title":"Proc 30th Int Conf Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12693"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2008.01122.x"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"ref12","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":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073608"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141926"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2614028.2615401"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2988458.2988473"},{"key":"ref19","first-page":"806","article-title":"CNN features off-the-shelf: An astounding baseline for recognition","author":"sharif razavian","year":"2014","journal-title":"Proc IEEE Conf Comp Vis Pattern Recognit"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref3","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.543"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2015.2480802"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2015.112"},{"key":"ref46","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref48","article-title":"ShapeNet: An information-rich 3D model repository","author":"chang","year":"2015","journal-title":"arXiv 1512 03012"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref41","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1109\/TPAMI.2009.65","article-title":"Learning a hierarchical deformable template for rapid deformable object parsing","volume":"32","author":"zhu","year":"2010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.316"}],"container-title":["IEEE Transactions on Visualization and Computer Graphics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/2945\/9103189\/08580422.pdf?arnumber=8580422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:51:31Z","timestamp":1641988291000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8580422\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,1]]},"references-count":60,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tvcg.2018.2887262","relation":{},"ISSN":["1077-2626","1941-0506","2160-9306"],"issn-type":[{"value":"1077-2626","type":"print"},{"value":"1941-0506","type":"electronic"},{"value":"2160-9306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,1]]}}}