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The creation of manufacturable and editable 3D shapes through Computer-Aided Design (CAD) remains a highly manual and time-consuming task, hampered by the complex topology of boundary representations of 3D solids and unintuitive design tools.
Topological structures for geometric modeling (Boundary representation, manifold, radial edge structure)
Kevin J Weiler · 1986
Earlier work this paper cites.
Toward intelligent cad systems
Setsuo Ohsuga · 1989
Earlier work this paper cites.
Engineering drawing processing and vectorization system
Vijay Nagasamy and Noshir A Langrana · 1990
Earlier work this paper cites.
From engineering drawings to 3d cad models: are we ready now?
Dov Dori and Karl Tombre · 1995
Earlier work this paper cites.
A cad model based system for object recognition
Jharna Majumdar and AG Seethalakshmy · 1997
Earlier work this paper cites.
Using geometric constraints to capture design intent
Holly K Ault · 1999
Earlier work this paper cites.
Automated cad conversion with the machine drawing understanding system: concepts, algorithms, and performance
Dov Dori and Liu Wenyin · 1999
Earlier work this paper cites.
Feature-based reverse engineering of mechanical parts
William B Thompson, Jonathan C Owen, HJ de St Germain, Stevan R Stark, and Thomas C Henderson · 1999
Earlier work this paper cites.
Ten challenges in computer-aided design
Les A Piegl · 2005
Earlier work this paper cites.
NVIDIA CUDA Compute Unified Device Architecture Programming Guide
Onshape · 2007
Earlier work this paper cites.
Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities
Christophe Geuzaine and Jean-François Remacle · 2009
Earlier work this paper cites.
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
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Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Attention is all you need
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Earlier work this paper cites.
O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
Earlier work this paper cites.
Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
Earlier work this paper cites.
Meshnet: Mesh neural network for 3d shape representation
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Tero Karras, Samuli Laine, and Timo Aila · 2019
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Pointdan: A multi-scale 3d domain adaption network for point cloud representation
Can Qin, Haoxuan You, Lichen Wang, C-C Jay Kuo, and Yun Fu · 2019
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Generating diverse high-fidelity images with vq-vae-2
Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences
Karl DD Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik · 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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Brepgen: A b-rep generative diffusion model with structured latent geometry
Xiang Xu, Joseph G Lambourne, Pradeep Kumar Jayaraman, Zhengqing Wang, Karl DD Willis, and Yasutaka Furukawa · 2021
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Hierarchical cadnet: Learning from b-reps for machining feature recognition
Andrew R Colligan, Trevor T Robinson, Declan C Nolan, Yang Hua, and Weijuan Cao · 2022
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Roca: Robust cad model retrieval and alignment from a single image
Can Gümeli, Angela Dai, and Matthias Nießner · 2022
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Language models are few-shot learners
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Graph representation of 3d cad models for machining feature recognition with deep learning
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Denoising diffusion probabilistic models
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Skexgen: Autoregressive generation of cad construction sequences with disentangled codebooks
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Shap-e: Generating conditional 3d implicit functions
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Gecco: Geometrically-conditioned point diffusion models
Michał J Tyszkiewicz, Pascal Fua, and Eduard Trulls · 2023
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Diffcad: Weakly-supervised probabilistic cad model retrieval and alignment from an rgb image
Daoyi Gao, Dávid Rozenberszki, Stefan Leutenegger, and Angela Dai · 2024
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Contrastcad: Contrastive learning-based representation learning for computer-aided design models
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Jing Yu Koh, Daniel Fried, and Russ R Salakhutdinov · 2024
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Representation learning for sequential volumetric design tasks
Md Ferdous Alam, Yi Wang, Chin-Yi Cheng, and Jieliang Luo · 2025
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