Geometric Deep Learning for Computer-Aided Design: A Survey
Negar Heidari Alexandros Iosifidis
Geometric Deep Learning techniques have become a transformative force in the
field of Computer-Aided Design (CAD), and have the potential to revolutionize
how designers and engineers approach and enhance the design process. By
harnessing the power of machine learning-based methods, CAD designers can
optimize their workflows, save time and effort while making better informed
decisions, and create designs that are both innovative and practical. The
ability to process the CAD designs represented by geometric data and to analyze
their encoded features enables the identification of similarities among diverse
CAD models, the proposition of alternative designs and enhancements, and even
the generation of novel design alternatives. This survey offers a comprehensive
overview of learning-based methods in computer-aided design across various
categories, including similarity analysis and retrieval, 2D and 3D CAD model
synthesis, and CAD generation from point clouds. Additionally, it provides a
complete list of benchmark datasets and their characteristics, along with
open-source codes that have propelled research in this domain. The final
discussion delves into the challenges prevalent in this field, followed by
potential future research directions in this rapidly evolving field.