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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:p>\n            Recovering the geometry of an object from a single depth image is an interesting yet challenging problem. While previous learning based approaches have demonstrated promising performance, they don\u2019t fully explore spatial relationships of objects, which leads to unfaithful and incomplete 3D reconstruction. To address these issues, we propose a\n            <jats:bold>Spatial Relationship Preserving Adversarial Network (SRPAN)<\/jats:bold>\n            consisting of\n            <jats:bold>3D Capsule Attention Generative Adversarial Network (3DCAGAN)<\/jats:bold>\n            and\n            <jats:bold>2D Generative Adversarial Network (2DGAN)<\/jats:bold>\n            for coarse-to-fine 3D reconstruction from a single depth view of an object. Firstly, 3DCAGAN predicts the coarse geometry using an encoder-decoder based generator and a discriminator. The generator encodes the input as latent capsules represented as stacked activity vectors with local-to-global relationships (i.e., the contribution of components to the whole shape), and then decodes the capsules by modeling local-to-local relationships (i.e., the relationships among components) in an attention mechanism. Afterwards, 2DGAN refines the local geometry slice-by-slice, by using a generator learning a global structure prior as guidance, and stacked discriminators enforcing local geometric constraints. Experimental results show that SRPAN not only outperforms several state-of-the-art methods by a large margin on both synthetic datasets and real-world datasets, but also reconstructs unseen object categories with a higher accuracy.\n          <\/jats:p>","DOI":"10.1145\/3506733","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T10:31:58Z","timestamp":1646389918000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-8197","authenticated-orcid":false,"given":"Caixia","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Multimedia &amp; Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehui","family":"Kong","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia &amp; Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaofan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia &amp; Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghua","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia &amp; Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia &amp; Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3177756"},{"key":"e_1_3_1_3_2","first-page":"15880","volume-title":"CVPR","author":"Bechtold Jan","year":"2021","unstructured":"Jan Bechtold, Maxim Tatarchenko, Volker Fischer, and Thomas Brox. 2021. 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