{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T17:17:00Z","timestamp":1673457420145},"reference-count":81,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T00:00:00Z","timestamp":1573171200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"\n The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets or large volumes of realistic training data. A key challenge towards this goal is how to accommodate diverse shape variations, including both continuous deformations of parts as well as structural or discrete alterations which add to, remove from, or modify the shape constituents and compositional structure. Such object structure can typically be organized into a hierarchy of constituent object parts and relationships, represented as a hierarchy of\n n<\/jats:italic>\n -ary graphs. We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such\n n<\/jats:italic>\n -ary graphs, (ii) can be robustly trained on large and complex shape families, and (iii) be used to generate a great diversity of realistic structured shape geometries. Technically, we accomplish this by drawing inspiration from recent advances in graph neural networks to propose an order-invariant encoding of\n n<\/jats:italic>\n -ary graphs, considering jointly both part geometry and inter-part relations during network training. We extensively evaluate the quality of the learned latent spaces for various shape families and show significant advantages over baseline and competing methods. The learned latent spaces enable several structure-aware geometry processing applications, including shape generation and interpolation, shape editing, or shape structure discovery directly from un-annotated images, point clouds, or partial scans.\n <\/jats:p>","DOI":"10.1145\/3355089.3356527","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T20:27:58Z","timestamp":1573244878000},"page":"1-19","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":73,"title":["StructureNet"],"prefix":"10.1145","volume":"38","author":[{"given":"Kaichun","family":"Mo","sequence":"first","affiliation":[{"name":"Stanford University"}]},{"given":"Paul","family":"Guerrero","sequence":"additional","affiliation":[{"name":"University College London"}]},{"given":"Li","family":"Yi","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Hao","family":"Su","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}]},{"given":"Peter","family":"Wonka","sequence":"additional","affiliation":[{"name":"KAUST"}]},{"given":"Niloy J.","family":"Mitra","sequence":"additional","affiliation":[{"name":"University College London and Adobe Research"}]},{"given":"Leonidas J.","family":"Guibas","sequence":"additional","affiliation":[{"name":"Stanford University and Facebook AI Research"}]}],"member":"320","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Learning representations and generative models for 3d point clouds. arXiv preprint arXiv:1707.02392","author":"Achlioptas Panos","year":"2017","unstructured":"Panos Achlioptas , Olga Diamanti , Ioannis Mitliagkas , and Leonidas Guibas . 2017. 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