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.
翻译:在计算机辅助设计( CAD), “ 反向工程师” 创建 3D 形状所用的模型步骤的能力是一个长期寻求的目标。 这个过程可以分解成两个子问题: 将输入网状或点云变成边界代表( 或 B- rep), 然后推断构建此 B- rep 的行业标准 CAD 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。 我们的方法的核心是一个新的几何表示法: 区域图。 区是扩大所有 B- Rep 脸和与它们隔开空间形成的坚固区域组; 区域图将这些区域作为它的节点, 其边际显示它们之间的几度对相匹配。 Zone 图形让我们与以前使用 CSG 和 等分数原始系统的工作不同, 重点是由草图 + Exrude + Boolean 操作组成的 CAD 程序, 这是CADD 中常见的。 我们用时间图描述我们的问题, 是要在直方 方向网络中进行快速的搜索, 我们用直径方 显示我们目前的平方 的进度图 。