In computer-aided design (CAD), the ability to "reverse engineer" the modeling steps used to create 3D shapes is a long-sought-after goal. This process can be decomposed into two sub-problems: converting an input mesh or point cloud into a boundary representation (or B-rep), and then inferring modeling operations which construct this B-rep. In this paper, we present a new system for solving the second sub-problem. Central to our approach is a new geometric representation: the zone graph. Zones are the set of solid regions formed by extending all B-Rep faces and partitioning space with them; a zone graph has these zones as its nodes, with edges denoting geometric adjacencies between them. Zone graphs allow us to tractably work with industry-standard CAD operations, unlike prior work using CSG with parametric primitives. We focus on CAD programs consisting of sketch + extrude + Boolean operations, which are common in CAD practice. We phrase our problem as search in the space of such extrusions permitted by the zone graph, and we train a graph neural network to score potential extrusions in order to accelerate the search. We show that our approach outperforms an existing CSG inference baseline in terms of geometric reconstruction accuracy and reconstruction time, while also creating more plausible modeling sequences.

中文翻译：

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使用区域图推断CAD建模序列

在计算机辅助设计（CAD）中，“逆向工程”用于创建3D形状的建模步骤的能力是人们长期以来追求的目标。该过程可以分解为两个子问题：将输入网格或点云转换为边界表示（或B-rep），然后推断构造该B-rep的建模操作。在本文中，我们提出了一个解决第二个子问题的新系统。我们方法的核心是一种新的几何表示形式：区域图。区域是通过扩展所有B-Rep面并与其分隔空间而形成的一组实心区域；区域图以这些区域为节点，边缘表示它们之间的几何邻接。区域图使我们能够轻松地处理行业标准的CAD操作，这与之前使用带有参数基元的CSG的工作不同。我们专注于CAD程序，该程序由草图+拉伸+布尔运算组成，这在CAD实践中很常见。我们将问题表达为在区域图允许的此类拉伸空间中进行搜索，并且我们训练图神经网络对潜在的拉伸进行评分，以加快搜索速度。我们表明，在几何重建精度和重建时间方面，我们的方法优于现有的CSG推理基准，同时还创建了更合理的建模序列。