Self-supervised Graph Neural Network for Mechanical CAD Retrieval

TL;DR

This paper introduces GC-CAD, a self-supervised graph neural network approach for mechanical CAD retrieval that models raw CAD files directly, improving accuracy and efficiency without manual labeling.

Contribution

The paper presents a novel self-supervised contrastive graph neural network method for CAD retrieval that directly processes parameterized CAD models, overcoming limitations of existing techniques.

Findings

Achieves significant accuracy improvements over baseline methods.

Provides up to 100 times efficiency enhancement.

Effectively models geometric and topological CAD features.

Abstract

CAD (Computer-Aided Design) plays a crucial role in mechanical industry, where large numbers of similar-shaped CAD parts are often created. Efficiently reusing these parts is key to reducing design and production costs for enterprises. Retrieval systems are vital for achieving CAD reuse, but the complex shapes of CAD models are difficult to accurately describe using text or keywords, making traditional retrieval methods ineffective. While existing representation learning approaches have been developed for CAD, manually labeling similar samples in these methods is expensive. Additionally, CAD models' unique parameterized data structure presents challenges for applying existing 3D shape representation learning techniques directly. In this work, we propose GC-CAD, a self-supervised contrastive graph neural network-based method for mechanical CAD retrieval that directly models parameterized CAD raw files. GC-CAD consists of two key modules: structure-aware representation learning and contrastive graph learning framework. The method leverages graph neural networks to extract both geometric and topological information from CAD models, generating feature representations. We then introduce a simple yet effective contrastive graph learning framework approach, enabling the model to train without manual labels and generate retrieval-ready representations. Experimental results on four datasets including human evaluation demonstrate that the proposed method achieves significant accuracy improvements and up to 100 times efficiency improvement over the baseline methods.