  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.
