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The success of Transformer-based models has encouraged many researchers to learn CAD models using sequence-based approaches.
Adam: A method for stochastic optimization
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina, Li Yi, and Leonidas J Guibas · 2019
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Structurenet: hierarchical graph networks for 3d shape generation
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Abc: A big cad model dataset for geometric deep learning
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Shapeassembly: Learning to generate programs for 3d shape structure synthesis
R. Kenny Jones, Theresa Barton, Xianghao Xu, Kai Wang, Ellen Jiang, Paul Guerrero, Niloy Mitra, and Daniel Ritchie · 2020
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Ucsg-net-unsupervised discovering of constructive solid geometry tree
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Sketch2cad: Sequential cad modeling by sketching in context
Changjian Li, Hao Pan, Adrien Bousseau, and Niloy J Mitra · 2020
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Big self-supervised models are strong semi-supervised learners
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Momentum contrast for unsupervised visual representation learning
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Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Skexgen: Autoregressive generation of cad construction sequences with disentangled codebooks
Xiang Xu, Karl DD Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, and Yasutaka Furukawa · 2022
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Reconstructing editable prismatic cad from rounded voxel models
Joseph George Lambourne, Karl Willis, Pradeep Kumar Jayaraman, Longfei Zhang, Aditya Sanghi, and Kamal Rahimi Malekshan · 2022
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Complexgen: Cad reconstruction by b-rep chain complex generation
Haoxiang Guo, Shilin Liu, Hao Pan, Yang Liu, Xin Tong, and Baining Guo · 2022
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Free2cad: Parsing freehand drawings into cad commands
Changjian Li, Hao Pan, Adrien Bousseau, and Niloy J. Mitra · 2022
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Discovering design concepts for cad sketches
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Inferring cad modeling sequences using zone graphs
Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl DD Willis, and Daniel Ritchie · 2021
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Deepcad: A deep generative network for computer-aided design models
Rundi Wu, Chang Xiao, and Changxi Zheng · 2021
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Uv-net: Learning from boundary representations
Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne, Karl D. D. Willis, Thomas Davies, Hooman Shayani, and Nigel Morris · 2021
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Brepnet: A topological message passing system for solid models
Joseph G Lambourne, Karl DD Willis, Pradeep Kumar Jayaraman, Aditya Sanghi, Peter Meltzer, and Hooman Shayani · 2021
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Vitruvion: A generative model of parametric cad sketches
Ari Seff, Wenda Zhou, Nick Richardson, and Ryan P Adams · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Hao Pan Yuezhi Yang · 2022
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Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Wen-tau Yih, Yoon Kim, and James Glass · 2022
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Hierarchical neural coding for controllable cad model generation
Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne, Karl D.D. Willis, and Yasutaka Furukawa · 2023
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Multicad: Contrastive representation learning for multi-modal 3d computer-aided design models
Weijian Ma, Minyang Xu, Xueyang Li, and Xiangdong Zhou · 2023
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Dˆ2csg: Unsupervised learning of compact csg trees with dual complements and dropouts
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