{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:23:59Z","timestamp":1761805439429,"version":"3.37.3"},"reference-count":28,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T00:00:00Z","timestamp":1590624000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["18510760300","1908085MF178","gxyqZD2019069"],"award-info":[{"award-number":["18510760300","1908085MF178","gxyqZD2019069"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["18510760300","1908085MF178","gxyqZD2019069"],"award-info":[{"award-number":["18510760300","1908085MF178","gxyqZD2019069"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Excellent Young Talents Support Program Project","award":["18510760300","1908085MF178","gxyqZD2019069"],"award-info":[{"award-number":["18510760300","1908085MF178","gxyqZD2019069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,5,28]]},"abstract":"<jats:p>The advancement of low-cost RGB-D and LiDAR three-dimensional (3D) sensors has permitted the obtainment of the 3D model easier in real-time. However, making intricate 3D features is crucial for the advancement of 3D object classifications. The existing volumetric voxel-based CNN approaches have achieved remarkable progress, but they generate huge computational overhead that limits the extraction of global features at higher resolutions of 3D objects. In this paper, a low-cost 3D volumetric deep convolutional neural network is proposed for 3D object classification based on joint multiscale hierarchical and subvolume supervised learning strategies. Our proposed deep neural network inputs 3D data, which are preprocessed by implementing memory-efficient octree representation, and we propose to limit the full layer octree depth to a certain level based on the predefined input volume resolution for storing high-precision contour features. Multiscale features are concatenated from multilevel octree depths inside the network, aiming to adaptively generate high-level global features. The strategy of the subvolume supervision approach is to train the network on subparts of the 3D object in order to learn local features. Our framework has been evaluated with two publicly available 3D repositories. Experimental results demonstrate the effectiveness of our proposed method where the classification accuracy is improved in comparison to existing volumetric approaches, and the memory consumption ratio and run-time are significantly reduced.<\/jats:p>","DOI":"10.1155\/2020\/5851465","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T23:32:03Z","timestamp":1590708723000},"page":"1-17","source":"Crossref","is-referenced-by-count":8,"title":["A New Volumetric CNN for 3D Object Classification Based on Joint Multiscale Feature and Subvolume Supervised Learning Approaches"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2001-1922","authenticated-orcid":true,"given":"A. A. M.","family":"Muzahid","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"},{"name":"Institute of Smart City, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanggen","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"},{"name":"Institute of Smart City, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Huangshan University, Anhui 245041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"first-page":"1097","volume-title":"ImageNet classification with deep convolutional neural networks","year":"2012","key":"2"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8258275"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12740"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2015.2480802"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.075"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2017.10.007"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.240"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2011.02058.x"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1017-7"},{"volume":"173","year":"2011","key":"30"},{"journal-title":"Computational Intelligence and Neuroscience","year":"May 2020","key":"31"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0824-y"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13536"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2816821"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018481"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2778764"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582532"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2605920"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0281-6"},{"key":"43","first-page":"223","volume-title":"Deep learning 3D shape surfaces using geometry images","volume":"9910","year":"2016"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2875512"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2904460"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.007"},{"key":"58","doi-asserted-by":"publisher","DOI":"10.1016\/0146-664x(82)90104-6"},{"year":"2019","key":"62"},{"key":"64","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5259129"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/5851465.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/5851465.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/5851465.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T23:31:21Z","timestamp":1591918281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2020\/5851465\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,28]]},"references-count":28,"alternative-id":["5851465","5851465"],"URL":"https:\/\/doi.org\/10.1155\/2020\/5851465","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2020,5,28]]}}}