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Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives.
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Procedural Modeling of Cities. In SIGGRAPH
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Converting 3D Furniture Models to Fabricatable Parts and Connectors
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Learning design patterns with Bayesian grammar induction. In UIST
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Bayesian Grammar Learning for Inverse Procedural Modeling. In CVPR
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Auto-Encoding Variational Bayes. In International Conference on Learning Representations (ICLR)
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3D ShapeNets: A Deep Representation for Volumetric Shapes. In Computer Vision and Pattern Recognition
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Proceduralization for Editing 3D Architectural Models. In 2016 Fourth International Conference on 3D Vision (3DV)
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Interactive Sketching of Urban Procedural Models
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Playing for data: Ground truth from computer games. In European conference on computer vision . Springer, 102–118
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Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances in Neural Information Processing Systems (NeurIPS)
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. 2016 · 2016
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A point set generation network for 3D object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition . 605–613
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In NeurIPS
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Inferring and Executing Programs for Visual Reasoning. In ICCV
Procedural Modeling of a Building from a Single Image
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Example-based Authoring of Procedural Modeling Programs with Structural and Continuous Variability. In EUROGRAPHICS
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CSGNet: Neural Shape Parser for Constructive Solid Geometry. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Gibson env: real-world perception for embodied agents. In CVPR
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SCORES: Shape Composition with Recursive Substructure Priors
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Renjiao Yi, and Hao Zhang. 2018 · 2018
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Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017 · 2017
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Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. 2017 · 2017
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AI2-THOR: An Interactive 3D Environment for Visual AI
Eric Kolve, Roozbeh Mottaghi, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi. 2017 · 2017
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GRASS: Generative recursive autoencoders for shape structures
Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, and Leonidas Guibas. 2017 · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems . 5099–5108
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017 · 2017
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ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
Minhyuk Sung, Hao Su, Vladimir G. Kim, Siddhartha Chaudhuri, and Leonidas Guibas. 2017 · 2017
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Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
Yinda Zhang, Shuran Song, Ersin Yumer, Manolis Savva, Joon-Young Lee, Hailin Jin, and Thomas Funkhouser. 2017 · 2017
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Learning to Generalize Kinematic Models to Novel Objects. In Proceedings of the Third Conference on Robot Learning
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Learning Implicit Fields for Generative Shape Modeling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Zhiqin Chen and Hao Zhang. 2019 · 2019
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Write, Execute, Assess: Program Synthesis with a REPL. In Advances in Neural Information Processing Systems (NeurIPS)
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SDM-NET: Deep Generative Network for Structured Deformable Mesh. In SIGGRAPH Asia
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Learning to Describe Scenes with Programs. In International Conference on Learning Representations (ICLR)
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Neurally-Guided Structure Inference. In International Conference on Machine Learning (ICML)
Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, and Jiajun Wu. 2019 · 2019
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Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019 · 2019
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Habitat: A Platform for Embodied AI Research. In The IEEE International Conference on Computer Vision (ICCV)
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. 2019 · 2019
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Learning to Infer and Execute 3D Shape Programs. In International Conference on Learning Representations (ICLR)
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu. 2019 · 2019
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Program Synthesis for Images using Tree-Structured LSTM. In PGR Workshop at NeurIPS
Chenghui Zhou, Chun-liang Li, and Barnabas Poczos. 2019 · 2019
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