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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.
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The Silver Lining
Clouds are shareable scientific instruments that create the potential for reproducibility by ensuring that all investigators have access to a common execution platform on which computational experiments can be repeated and compared. By virtue of the interface they present, they also lead to the creation of digital artifacts compatible with the cloud, such as images or orchestration templates, that go a long way—and sometimes all the way—to representing an experiment in a digital, repeatable form. In this article, I describe how we developed these natural advantages of clouds in the Chameleon testbed and argue that we should leverage them to create a digital research marketplace that would make repeating experiments as natural and viable part of research as sharing ideas via reading papers is today.
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- Award ID(s):
- 1743358
- PAR ID:
- 10195661
- Date Published:
- Journal Name:
- IEEE internet computing
- ISSN:
- 1941-0131
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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