Inferring CAD Modeling Sequences Using Zone Graphs

TL;DR

This paper introduces a novel zone graph representation and a graph neural network approach to infer CAD modeling sequences, improving accuracy and efficiency over previous methods for reverse engineering 3D shapes.

Contribution

The paper presents a new zone graph-based geometric representation and a neural network method for inferring CAD modeling sequences, focusing on sketch, extrude, and Boolean operations.

Findings

Outperforms existing CSG inference baseline in accuracy

Reduces reconstruction time significantly

Produces more plausible modeling sequences

Abstract

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.