1 code implementation • 28 Jan 2024 • Xiang Xu, Joseph G. Lambourne, Pradeep Kumar Jayaraman, Zhengqing Wang, Karl D. D. Willis, Yasutaka Furukawa
Starting from the root and progressing to the leaf, BrepGen employs Transformer-based diffusion models to sequentially denoise node features while duplicated nodes are detected and merged, recovering the B-Rep topology information.
1 code implementation • 30 Jun 2023 • Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne, Karl D. D. Willis, Yasutaka Furukawa
This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree.
Ranked #6 on
CAD Reconstruction
on Fusion 360 Gallery
(IoU metric)
1 code implementation • 25 Apr 2023 • Peter Meltzer, Joseph G. Lambourne, Daniele Grandi
In this work we propose that the natural language names designers use in Computer Aided Design (CAD) software are a valuable source of such knowledge, and that Large Language Models (LLMs) contain useful domain-specific information for working with this data as well as other CAD and engineering-related tasks.
no code implementations • 2 Sep 2022 • Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman, Longfei Zhang, Aditya Sanghi, Kamal Rahimi Malekshan
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications.
Ranked #5 on
CAD Reconstruction
on Fusion 360 Gallery
(IoU metric)
no code implementations • 11 Jul 2022 • Xiang Xu, Karl D. D. Willis, Joseph G. Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations.
no code implementations • 26 Mar 2022 • Pradeep Kumar Jayaraman, Joseph G. Lambourne, Nishkrit Desai, Karl D. D. Willis, Aditya Sanghi, Nigel J. W. Morris
Key to achieving this is our Indexed Boundary Representation that references B-rep vertices, edges and faces in a well-defined hierarchy to capture the geometric and topological relations suitable for use with machine learning.
2 code implementations • CVPR 2022 • Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
1 code implementation • CVPR 2022 • Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
no code implementations • 19 Apr 2021 • Karl D. D. Willis, Pradeep Kumar Jayaraman, Joseph G. Lambourne, Hang Chu, Yewen Pu
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects.
2 code implementations • CVPR 2021 • Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman, Aditya Sanghi, Peter Meltzer, Hooman Shayani
Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications.
Ranked #1 on
B-Rep face segmentation
on Fusion 360 Gallery
1 code implementation • 5 Oct 2020 • Karl D. D. Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software.
1 code implementation • CVPR 2021 • Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne, Karl D. D. Willis, Thomas Davies, Hooman Shayani, Nigel Morris
We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models.