10 citations · 20 across the 7 of their papers we have counts for
25 papers
Self-Supervised Representation Learning for CAD
Benjamin T. Jones, Michael Hu, Vladimir G. Kim +1
The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in…
AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies
Benjamin Jones, Dalton Hildreth, Duowen Chen +3
Assembly modeling is a core task of computer aided design (CAD), comprising around one third of the work in a CAD workflow. Optimizing this process therefore represents a huge oppo…
Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung +3
We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a databas…
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
Zhiqin Chen, Vladimir G. Kim, Matthew Fisher +3
We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties…
COALESCE: Component Assembly by Learning to Synthesize Connections
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri +3
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections. To handle geometric and topolo…
Self-supervised Learning of Point Clouds via Orientation Estimation
Omid Poursaeed, Tianxing Jiang, Han Qiao +2
Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require ma…