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We present SAGNet, a structure-aware generative model for 3D shapes.
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Structure-aware Shape Processing. In ACM SIGGRAPH Courses . 13:1–13:21
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The shape variational autoencoder: A deep generative model of part-segmented 3D objects. In Computer Graphics Forum , Vol. 36. 1–12
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Learning Shape Abstractions by Assembling Volumetric Primitives
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Ming-Yu Liu and Oncel Tuzel. 2016 · 2016
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Pixel recurrent neural networks
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Learning Representations and Generative Models for 3D Point Clouds
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So-net: Self-organizing network for point cloud analysis. In Proc. IEEE Conf. on Computer Vision & Pattern Recognition . 9397–9406
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Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. 2018 · 2018
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Learning to Generate the" Unseen" via Part Synthesis and Composition
Nadav Schor, Oren Katzir, Hao Zhang, and Daniel Cohen-Or. 2018 · 2018
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Improving Variational Encoder-Decoders in Dialogue Generation
Xiaoyu Shen, Hui Su, Shuzi Niu, and Vera Demberg. 2018 · 2018
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Global-to-Local Generative Model for 3D Shapes
Hao Wang, Nadav Schor, Ruizhen Hu, Haibin Huang, Daniel Cohen-Or, and Hui Huang. 2018 · 2018
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FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation. In Proc. IEEE Conf. on Computer Vision & Pattern Recognition . 206–215
Yaoqing Yang, Cheng Feng, Yiru Shen, and Dong Tian. 2018 · 2018
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Augmenting CRFs with Boltzmann machine shape priors for image labeling. In Proc. IEEE Conf. on Computer Vision & Pattern Recognition . 2019–2026
Andrew Kae, Kihyuk Sohn, Honglak Lee, and Erik Learned-Miller. 2013 · 2026
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