Geometric Deep Learning for Computer-Aided Design: A Survey

2024-02-27

arXiv.org

AI Summary

This survey addresses the growing need for systematic understanding of geometric deep learning (GDL) in computer-aided design (CAD). We focus on three core challenges: design similarity analysis, 2D/3D model synthesis, and CAD reconstruction from point clouds or single/multi-view inputs. Methodologically, we propose the first task taxonomy tailored to CAD-specific GDL, rigorously delineating paradigm boundaries; consolidate over 12 benchmark datasets and 50+ open-source implementations; and unify diverse techniques—including graph neural networks, point cloud processing, mesh learning, generative modeling, and multimodal representation—into a coherent methodological framework. Our analysis identifies critical bottlenecks and open problems, while providing an extensible research roadmap and practical implementation guidelines. The work significantly advances the efficiency, generalizability, and real-world applicability of intelligent CAD design systems.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: 3D Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Applying Geometric Deep Learning to enhance CAD design processes
• Optimizing CAD workflows using machine learning-based methods
• Analyzing geometric data for similarity and novel design generation

Innovation

• Geometric Deep Learning transforms CAD design processes
• Machine learning optimizes workflows and decision-making in CAD
• Learning-based methods enable CAD model synthesis and generation