no code implementations • 25 Jan 2024 • Milin Kodnongbua, Lawrence H. Curtis, Adriana Schulz
This paper introduces a novel automated system for generating architecture schematic designs aimed at streamlining complex decision-making at the multifamily real estate development project's outset.
no code implementations • 25 Jul 2023 • Liane Makatura, Michael Foshey, Bohan Wang, Felix HähnLein, Pingchuan Ma, Bolei Deng, Megan Tjandrasuwita, Andrew Spielberg, Crystal Elaine Owens, Peter Yichen Chen, Allan Zhao, Amy Zhu, Wil J Norton, Edward Gu, Joshua Jacob, Yifei Li, Adriana Schulz, Wojciech Matusik
The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design.
no code implementations • CVPR 2023 • Benjamin T. Jones, Michael Hu, Vladimir G. Kim, Adriana Schulz
Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep).
no code implementations • 2 Aug 2022 • James Noeckel, Benjamin T. Jones, Karl Willis, Brian Curless, Adriana Schulz
We describe our work on inferring the degrees of freedom between mated parts in mechanical assemblies using deep learning on CAD representations.
1 code implementation • 21 Jul 2021 • James Noeckel, Haisen Zhao, Brian Curless, Adriana Schulz
We propose a novel method to generate fabrication blueprints from images of carpentered items.
no code implementations • 25 May 2021 • Benjamin Jones, Dalton Hildreth, Duowen Chen, Ilya Baran, Vladimir G. Kim, Adriana Schulz
To train our system, we compiled the first large scale dataset of BREP CAD assemblies, which we are releasing along with benchmark mate prediction tasks.