The Boundary representation (B-rep) format is the de-facto shape representation in computer-aided design (CAD) to model solid and sheet objects. Recent approaches to generating CAD models have focused on learning sketch-and-extrude modeling sequences that are executed by a solid modeling kernel in postprocess to recover a B-rep. In this paper we present a new approach that enables learning from and synthesizing B-reps without the need for supervision through CAD modeling sequence data. Our method SolidGen, is an autoregressive neural network that models the B-rep directly by predicting the vertices, edges, and faces using Transformer-based and pointer neural networks. Key to achieving this is our Indexed Boundary Representation that references B-rep vertices, edges and faces in a well-defined hierarchy to capture the geometric and topological relations suitable for use with machine learning. SolidGen can be easily conditioned on contexts e.g., class labels, images, and voxels thanks to its probabilistic modeling of the B-rep distribution. We demonstrate qualitatively, quantitatively, and through perceptual evaluation by human subjects that SolidGen can produce high quality, realistic CAD models.

翻译：边界表示（B-rep）格式是计算机辅助设计（CAD）中对实体和片体对象进行建模的标准形状表示。现有生成CAD模型的方法通常侧重于学习草图与拉伸建模序列，这些序列在后处理中通过实体建模内核执行以恢复B-rep。本文提出了一种新方法，能够在无需CAD建模序列数据监督的情况下，从B-rep中学习并进行合成。我们的方法SolidGen是一种自回归神经网络，通过使用基于Transformer和指针神经网络预测顶点、边和面，直接对B-rep进行建模。实现这一点的关键在于我们提出的索引边界表示，该表示在定义良好的层次结构中引用B-rep的顶点、边和面，以捕获适用于机器学习的几何与拓扑关系。得益于其对B-rep分布的概率建模，SolidGen可轻松基于上下文（如类别标签、图像和体素）进行条件生成。通过定性、定量分析及人类主体的感知评估，我们证明了SolidGen能够生成高质量、逼真的CAD模型。