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The Boundary representation (B-rep) format is the de-facto shape representation in computer-aided design (CAD) to model solid and sheet objects.
Winged edge polyhedron representation
Bruce G. Baumgart · 1972
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Geometric modeling of solid objects by using a face adjacency graph representation
Silvia Ansaldi, Leila De Floriani, and Bianca Falcidieno · 1985
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Topological structures for geometric modeling
K.J. Weiler · 1986
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Constrained delaunay triangulations
L. P. Chew · 1987
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Real-time rendering of trimmed surfaces
Alyn Rockwood, Kurt Heaton, and Tom Davis · 1989
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A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
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Automated ejectability analysis and parting surface generation for mold tool design
Rahul Bhargava, Lee Elliot Weiss, Friedrich B Prinz, et al · 1991
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Automatic Generation of Parting Surfaces and Mold Halves , volume ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium of International Design Engineering Technical Conferences and Computers and Information in Engineering Conference , 09 1995. ASME
1995
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Geometric constraint solver
William Bouma, Ioannis Fudos, Christoph Hoffmann, Jiazhen Cai, and Robert Paige · 1995
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Cad data repair
Geoffrey Butlin and Clive Stops · 1996
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Partial entity structure: A compact non-manifold boundary representation based on partial topological entities
Sang Hun Lee and Kunwoo Lee · 2001
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CAD model robustness assessment and repair
Armand Daryoush Assadi · 2003
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pythonocc, 3d cad/cae/plm development framework for the python programming language
Thomas Paviot · 2008
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A Computational Hybrid Method for Self-Intersection Free Offsetting of CAD Geometry
Garrett Bodily · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel · 2016
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Programming language tools and techniques for 3d printing
Chandrakana Nandi, Anat Caspi, Dan Grossman, and Zachary Tatlock · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Inversecsg: Automatic conversion of 3d models to csg trees
Tao Du, Jeevana Priya Inala, Yewen Pu, Andrew Spielberg, Adriana Schulz, Daniela Rus, Armando Solar-Lezama, and Wojciech Matusik · 2018
Cited alongside, same era.
Functional programming for compiling and decompiling computer-aided design
Chandrakana Nandi, James R Wilcox, Pavel Panchekha, Taylor Blau, Dan Grossman, and Zachary Tatlock · 2018
Cited alongside, same era.
Csgnet: Neural shape parser for constructive solid geometry
Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji · 2018
Cited alongside, same era.
Write, execute, assess: Program synthesis with a repl
Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama · 2019
Cited alongside, same era.
Abc: A big cad model dataset for geometric deep learning
Parsenet: A parametric surface fitting network for 3d point clouds
Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, and Radomír Měch · 2020
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On the role of graph theory apparatus in a cad modeling kernel
Sergey Slyadnev, Alexander Malyshev, Andrey Voevodin, and Vadim Turlapov · 2020
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Pie-net: Parametric inference of point cloud edges
Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, and Hao Zhang · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
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Computer-aided design as language
Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, and Stefano Saliceti · 2021
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Uv-net: Learning from boundary representations
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Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Machine learning in the aws cloud: Add intelligence to applications with amazon sagemaker and amazon rekognition, 2019
Abhishek Mishra · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Learning to infer and execute 3d shape programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
Cited alongside, same era.
Graph representation of 3d cad models for machining feature recognition with deep learning
Weijuan Cao, Trevor Robinson, Yang Hua, Flavien Boussuge, Andrew R. Colligan, and Wanbin Pan · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Cited alongside, same era.
Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne, Karl D.D. Willis, Thomas Davies, Hooman Shayani, and Nigel Morris · 2021
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Automate: a dataset and learning approach for automatic mating of cad assemblies
Benjamin Jones, Dalton Hildreth, Duowen Chen, Ilya Baran, Vladimir G Kim, and Adriana Schulz · 2021
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Brepnet: A topological message passing system for solid models
Joseph G. Lambourne, Karl D.D. Willis, Pradeep Kumar Jayaraman, Aditya Sanghi, Peter Meltzer, and Hooman Shayani · 2021
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Sketchgen: Generating constrained cad sketches
Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy Mitra, Leonidas Guibas, and Peter Wonka · 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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Learning manifold patch-based representations of man-made shapes
Dmitriy Smirnov, Mikhail Bessmeltsev, and Justin Solomon · 2021
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Point2cyl: Reverse engineering 3d objects from point clouds to extrusion cylinders
Mikaela Angelina Uy, Yen-yu Chang, Minhyuk Sung, Purvi Goel, Joseph Lambourne, Tolga Birdal, and Leonidas J. Guibas · 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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Inferring cad modeling sequences using zone graphs
Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl D.D. Willis, and Daniel Ritchie · 2021
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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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Reconstructing editable prismatic cad from rounded voxel models
Joseph G. Lambourne, Karl D.D. Willis, Pradeep Kumar Jayaraman, Longfei Zhang, Aditya Sanghi, and Kamal Rahimi Malekshan · 2022
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Neural face identification in a 2d wireframe projection of a manifold object
Kehan Wang, Jia Zheng, and Zihan Zhou · 2022
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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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