{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:38:26Z","timestamp":1778081906063,"version":"3.51.4"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T00:00:00Z","timestamp":1562889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61761146002, 61861130365"],"award-info":[{"award-number":["61761146002, 61861130365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"973 Program","doi-asserted-by":"crossref","award":["2015CB352501"],"award-info":[{"award-number":["2015CB352501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"LHTD","award":["20170003"],"award-info":[{"award-number":["20170003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2019,8,31]]},"abstract":"<jats:p>We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure) are jointly learned and embedded in a latent space by an autoencoder. The encoder intertwines the geometry and structure features into a single latent code, while the decoder disentangles the features and reconstructs the geometry and structure of the 3D model. Our autoencoder consists of two branches, one for the structure and one for the geometry. The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code. This explicit intertwining of information enables separately controlling the geometry and the structure of the generated models. We evaluate the performance of our method and conduct an ablation study. We explicitly show that encoding of shapes accounts for both similarities in structure and geometry. A variety of quality results generated by SAGNet are presented.<\/jats:p>","DOI":"10.1145\/3306346.3322956","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T19:04:08Z","timestamp":1562958248000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["SAGNet"],"prefix":"10.1145","volume":"38","author":[{"given":"Zhijie","family":"Wu","sequence":"first","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Lin","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dani","family":"Lischinski","sequence":"additional","affiliation":[{"name":"The Hebrew University of Jerusalem"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"e_1_2_2_1_1","first-page":"265","article-title":"TensorFlow: A System for Large-Scale Machine Learning","volume":"16","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . TensorFlow: A System for Large-Scale Machine Learning .. In OSDI , Vol. 16. 265 -- 283 . Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. TensorFlow: A System for Large-Scale Machine Learning.. In OSDI, Vol. 16. 265--283.","journal-title":"OSDI"},{"key":"e_1_2_2_2_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. Learning Representations and Generative Models for 3D Point Clouds. arXiv preprint arXiv:1707.02392 ( 2017 ). Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2017. Learning Representations and Generative Models for 3D Point Clouds. arXiv preprint arXiv:1707.02392 (2017)."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601102"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K16-1002"},{"key":"e_1_2_2_5_1","unstructured":"Kyunghyun Cho Bart van Merri\u00ebnboer Dzmitry Bahdanau and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. (2014) 103--111.  Kyunghyun Cho Bart van Merri\u00ebnboer Dzmitry Bahdanau and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. (2014) 103--111."},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"e_1_2_2_7_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 ( 2014 ). Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601185"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_29"},{"key":"e_1_2_2_10_1","volume-title":"Danilo Jimenez Rezende, and Daan Wierstra","author":"Gregor Karol","year":"2015","unstructured":"Karol Gregor , Ivo Danihelka , Alex Graves , Danilo Jimenez Rezende, and Daan Wierstra . 2015 . DRAW : A recurrent neural network for image generation. (2015), 1462--1471. Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. 2015. DRAW: A recurrent neural network for image generation. (2015), 1462--1471."},{"key":"e_1_2_2_11_1","volume-title":"2017 International Conference on. IEEE, 263--272","author":"Gwak JunYoung","year":"2017","unstructured":"JunYoung Gwak , Christopher B Choy , Manmohan Chandraker , Animesh Garg , and Silvio Savarese . 2017 . Weakly supervised 3d reconstruction with adversarial constraint. In 3D Vision (3DV) , 2017 International Conference on. IEEE, 263--272 . JunYoung Gwak, Christopher B Choy, Manmohan Chandraker, Animesh Garg, and Silvio Savarese. 2017. Weakly supervised 3d reconstruction with adversarial constraint. In 3D Vision (3DV), 2017 International Conference on. IEEE, 263--272."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12694"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.8"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.263"},{"key":"e_1_2_2_15_1","volume-title":"Proc. Int. Conf. on Learning Representations.","author":"Diederik","unstructured":"Diederik P. Kingma and Max Welling. 2014. Auto-Encoding variational bayes . In Proc. Int. Conf. on Learning Representations. Diederik P. Kingma and Max Welling. 2014. Auto-Encoding variational bayes. In Proc. Int. Conf. on Learning Representations."},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00979"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073637"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818071"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294838"},{"key":"e_1_2_2_20_1","volume-title":"Proc. Int. Conf. on Neural Information Processing Systems. 469--477","author":"Liu Ming-Yu","year":"2016","unstructured":"Ming-Yu Liu and Oncel Tuzel . 2016 . Coupled generative adversarial networks . In Proc. Int. Conf. on Neural Information Processing Systems. 469--477 . Ming-Yu Liu and Oncel Tuzel. 2016. Coupled generative adversarial networks. In Proc. Int. Conf. on Neural Information Processing Systems. 469--477."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2614028.2615401"},{"key":"e_1_2_2_22_1","volume-title":"PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. arXiv preprint arXiv:1812.02713","author":"Mo Kaichun","year":"2018","unstructured":"Kaichun Mo , Shilin Zhu , Angel X Chang , Li Yi , Subarna Tripathi , Leonidas J Guibas , and Hao Su. 2018. PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. arXiv preprint arXiv:1812.02713 ( 2018 ). Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. 2018. PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. arXiv preprint arXiv:1812.02713 (2018)."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13240"},{"key":"e_1_2_2_24_1","unstructured":"Aaron van den Oord Nal Kalchbrenner and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. (2016) 1747--1756.   Aaron van den Oord Nal Kalchbrenner and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. (2016) 1747--1756."},{"key":"e_1_2_2_25_1","volume-title":"Proc. Int. Conf. on Neural Information Processing Systems. 4996--5004","author":"Rezende Danilo Jimenez","year":"2016","unstructured":"Danilo Jimenez Rezende , SM Ali Eslami , Shakir Mohamed , Peter Battaglia , Max Jaderberg , and Nicolas Heess . 2016 . Unsupervised learning of 3D structure from images . In Proc. Int. Conf. on Neural Information Processing Systems. 4996--5004 . Danilo Jimenez Rezende, SM Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess. 2016. Unsupervised learning of 3D structure from images. In Proc. Int. Conf. on Neural Information Processing Systems. 4996--5004."},{"key":"e_1_2_2_26_1","unstructured":"Danilo Jimenez Rezende Shakir Mohamed and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. (2014) 1278--1286.   Danilo Jimenez Rezende Shakir Mohamed and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. (2014) 1278--1286."},{"key":"e_1_2_2_27_1","unstructured":"Adam Roberts Jesse Engel and Douglas Eck. 2017. Hierarchical variational autoencoders for music. (2017).  Adam Roberts Jesse Engel and Douglas Eck. 2017. Hierarchical variational autoencoders for music. (2017)."},{"key":"e_1_2_2_28_1","volume-title":"Proc. Int. Conf. on Neural Information Processing Systems. 2234--2242","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans , Ian Goodfellow , Wojciech Zaremba , Vicki Cheung , Alec Radford , and Xi Chen . 2016 . Improved techniques for training gans . In Proc. Int. Conf. on Neural Information Processing Systems. 2234--2242 . Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training gans. In Proc. Int. Conf. on Neural Information Processing Systems. 2234--2242."},{"key":"e_1_2_2_29_1","volume-title":"Learning to Generate the\" Unseen\" via Part Synthesis and Composition. arXiv preprint arXiv:1811.07441","author":"Schor Nadav","year":"2018","unstructured":"Nadav Schor , Oren Katzir , Hao Zhang , and Daniel Cohen-Or . 2018. Learning to Generate the\" Unseen\" via Part Synthesis and Composition. arXiv preprint arXiv:1811.07441 ( 2018 ). Nadav Schor, Oren Katzir, Hao Zhang, and Daniel Cohen-Or. 2018. Learning to Generate the\" Unseen\" via Part Synthesis and Composition. arXiv preprint arXiv:1811.07441 (2018)."},{"key":"e_1_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Xiaoyu Shen Hui Su Shuzi Niu and Vera Demberg. 2018. Improving Variational Encoder-Decoders in Dialogue Generation. (2018).  Xiaoyu Shen Hui Su Shuzi Niu and Vera Demberg. 2018. Improving Variational Encoder-Decoders in Dialogue Generation. (2018).","DOI":"10.1609\/aaai.v32i1.11960"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.91"},{"key":"e_1_2_2_32_1","volume-title":"Proc. Int. Conf. on Neural Information Processing Systems. 656--664","author":"Socher Richard","year":"2012","unstructured":"Richard Socher , Brody Huval , Bharath Bath , Christopher D Manning , and Andrew Y Ng . 2012 . Convolutional-recursive deep learning for 3D object classification . In Proc. Int. Conf. on Neural Information Processing Systems. 656--664 . Richard Socher, Brody Huval, Bharath Bath, Christopher D Manning, and Andrew Y Ng. 2012. Convolutional-recursive deep learning for 3D object classification. In Proc. Int. Conf. on Neural Information Processing Systems. 656--664."},{"key":"e_1_2_2_33_1","volume-title":"Proc. Int. Conf. on Machine Learning. 129--136","author":"Socher Richard","year":"2011","unstructured":"Richard Socher , Cliff C Lin , Chris Manning , and Andrew Y Ng . 2011 . Parsing natural scenes and natural language with recursive neural networks . In Proc. Int. Conf. on Machine Learning. 129--136 . Richard Socher, Cliff C Lin, Chris Manning, and Andrew Y Ng. 2011. Parsing natural scenes and natural language with recursive neural networks. In Proc. Int. Conf. on Machine Learning. 129--136."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.230"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.160"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275025"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157106"},{"key":"e_1_2_2_38_1","volume-title":"Proc. IEEE Conf. on Computer Vision & Pattern Recognition. 1912--1920","author":"Wu Zhirong","year":"2015","unstructured":"Zhirong Wu , Shuran Song , Aditya Khosla , Fisher Yu , Linguang Zhang , Xiaoou Tang , and Jianxiong Xiao . 2015 . 3D shapenets: A deep representation for volumetric shapes . In Proc. IEEE Conf. on Computer Vision & Pattern Recognition. 1912--1920 . Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3D shapenets: A deep representation for volumetric shapes. In Proc. IEEE Conf. on Computer Vision & Pattern Recognition. 1912--1920."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"key":"e_1_2_2_40_1","volume-title":"Proc. Int. Conf. on Neural Information Processing Systems. 1696--1704","author":"Yan Xinchen","year":"2016","unstructured":"Xinchen Yan , Jimei Yang , Ersin Yumer , Yijie Guo , and Honglak Lee . 2016 . Perspective transformer nets: Learning single-view 3D object reconstruction without 3d supervision . In Proc. Int. Conf. on Neural Information Processing Systems. 1696--1704 . Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee. 2016. Perspective transformer nets: Learning single-view 3D object reconstruction without 3d supervision. In Proc. Int. Conf. on Neural Information Processing Systems. 1696--1704."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00029"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980238"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0795-4"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.103"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3306346.3322956","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3306346.3322956","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:44Z","timestamp":1750206344000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3306346.3322956"}},"subtitle":["structure-aware generative network for 3D-shape modeling"],"short-title":[],"issued":{"date-parts":[[2019,7,12]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,8,31]]}},"alternative-id":["10.1145\/3306346.3322956"],"URL":"https:\/\/doi.org\/10.1145\/3306346.3322956","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,12]]},"assertion":[{"value":"2019-07-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}