Inferring CAD Modeling Sequences Using Zone Graphs

Inferring CAD Modeling Sequences Using Zone Graphs
复制标题

DOI：
10.1109/cvpr46437.2021.00600
复制
发表时间：
2021-03
期刊：
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
--
通讯作者：
Xianghao Xu-;Wenzhe Peng;Chin-Yi Cheng;Karl D. D. Willis-Karl-D.-D.-Willis-2269914;Daniel Ritchie
Xianghao Xu-;Wenzhe Peng;Chin-Yi Cheng;Karl D. D. Willis-Karl-D.-D.-Willis-2269914;Daniel Ritchie
中科院分区：
其他
文献类型：
--
作者：
Xianghao Xu-;Wenzhe Peng;Chin-Yi Cheng;Karl D. D. Willis-Karl-D.-D.-Willis-2269914;Daniel Ritchie

文献摘要

相似文献

在计算机辅助设计（CAD）中，对用于创建三维形状的建模步骤进行“逆向工程”的能力是一个长期追求的目标。这个过程可以分解为两个子问题：将输入的网格或点云转换为边界表示（或B-rep），然后推断出构建此B-rep的建模操作。在本文中，我们提出了一个用于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：区域图。区域是通过延伸所有B-Rep面并用它们划分空间而形成的实体区域集合；区域图以这些区域为节点，边表示它们之间的几何邻接关系。与先前使用带有参数基元的CSG的工作不同，区域图使我们能够轻松处理行业标准的CAD操作。我们专注于由草图 + 拉伸 + 布尔运算组成的CAD程序，这些在CAD实践中很常见。我们将我们的问题表述为在区域图所允许的此类拉伸空间中的搜索，并且我们训练一个图神经网络来对潜在的拉伸进行评分，以加速搜索。我们表明，我们的方法在几何重建精度和重建时间方面优于现有的CSG推断基线，同时还创建了更合理的建模序列。
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.