Publication | IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2021

Inferring CAD Modeling Sequences using Zone Graphs

This paper introduces a new 3D representation, called the Zone Graph, that can be used with machine learning based approaches to tackle the long standing problem of reverse engineering.

Download publication


Inferring CAD Modeling Sequences using Zone Graphs

Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl D.D. Willis, Daniel Ritchie

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2021

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.

Related Resources



Task-Centric Application Switching: How and Why Knowledge Workers Switch Software Applications for a Single Task

This research studies task-centric application switching and…



XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python

This research introduces the XLB library, a scalable Python-based…



BOP-Elites: A Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions

An algorithm that efficiently tackles expensive black-box optimization…



Systems Design and Simulation

Predictive models of complex systems will require a more scalable,…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us