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.

提出了基于 Transformer 的 CAD 生成模型，能够自动编码和随机生成 3D 形状，为此创建了一个包含 178,238 个模型的 CAD 数据集，并公开该数据集以促进未来的研究。

DeepCAD：用于计算机辅助设计模型的深度生成网络

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

We present a sketch-based CAD modeling system, where users create objects
incrementally by sketching the desired shape edits, which our system
automatically translates to CAD operations. Our approach is motivated by the
close similarities between the steps industrial designers follow to draw 3D
shapes, and the operations CAD modeling systems offer to create similar shapes.
To overcome the strong ambiguity with parsing 2D sketches, we observe that in a
sketching sequence, each step makes sense and can be interpreted in the
\emph{context} of what has been drawn before. In our system, this context
corresponds to a partial CAD model, inferred in the previous steps, which we
feed along with the input sketch to a deep neural network in charge of
interpreting how the model should be modified by that sketch. Our deep network
architecture then recognizes the intended CAD operation and segments the sketch
accordingly, such that a subsequent optimization estimates the parameters of
the operation that best fit the segmented sketch strokes. Since there exists no
datasets of paired sketching and CAD modeling sequences, we train our system by
generating synthetic sequences of CAD operations that we render as line
drawings. We present a proof of concept realization of our algorithm supporting
four frequently used CAD operations. Using our system, participants are able to
quickly model a large and diverse set of objects, demonstrating Sketch2CAD to
be an alternate way of interacting with current CAD modeling systems.

本文提出了一种基于草图的 CAD 建模系统，通过深度神经网络对 CAD 操作进行识别，以解决解析二维草图模糊不清的问题，并使用合成的 CAD 操作序列进行训练。实验表明，该系统可以快速创建各种不同的对象，是一种与现有 CAD 建模系统交互的替代方式。