DeepCAD: A Deep Generative Network for Computer-Aided Design Models

By Rundi Wu, Chang Xiao, Changxi Zheng. IEEE International Conference on Computer Vision, 2021

350 citations recorded by the source corpus.

Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation— describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/57d71b30f2ff68bde4a0c50322bb93a5c3358ee0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Ali Farhadi
2. Jordi Salvador
3. Ludwig Schmidt
4. Aniruddha Kembhavi
5. Kiana Ehsani

In Computer Graphics and Computer-Aided Design

1. Ali Farhadi
2. Ludwig Schmidt
3. Matt Deitke
4. Eli VanderBilt
5. Aniruddha Kembhavi
6. Kiana Ehsani

In Computer Vision and Pattern Recognition

1. Aniruddha Kembhavi
2. Ali Farhadi
3. Roozbeh Mottaghi
4. Kristen Grauman
5. Luca Weihs
6. Luc Van Gool
7. Abhinav Kumar Gupta
8. Ludwig Schmidt

Citations in the corpus

• Objaverse: A Universe of Annotated 3D Objects (2022, methodology): “Datasets for CAD models, such as ModelNet [78] and DeepCAD [77], and ABC [35] do not include textures or materials, which limits their ability to represent objects that could plausibly be found in the real world.”