Masked Topology Modeling for Self-Supervised Learning on Parametric CAD

2026-07-22

AI Summary

This work addresses the scarcity of annotated parametric CAD data and the lack of effective self-supervised learning methods by proposing Masked Topological Modeling (MTM), a self-supervised pretraining approach tailored for native Boundary Representations (B-Reps). MTM leverages the face adjacency graph structure inherent in B-Reps for the first time, jointly optimizing masked edge geometry reconstruction with contrastive learning through a region-based masking objective. It introduces a BFS-connected-region masking strategy and CAD-aware data augmentation, integrated within a graph neural network framework enhanced by momentum contrastive learning. Extensive experiments demonstrate that MTM significantly outperforms existing methods across multiple CAD understanding benchmarks, confirming its effectiveness and strong generalization capability in low-label regimes.

Abstract

Computer aided design (CAD) is ubiquitous: virtually any modern object was designed using editable CAD tools. However, with the shortage of available CAD datasets in its native editable and parametric format, boundary representation (B-Rep), it is ever more important to develop data-efficient methods for this domain. We present a new self-supervised pretraining task, Masked Topology Modeling (MTM), that leverages the face-adjacency graph, an induced structure unique to B-reps that the encoder can be asked to reconstruct. MTM masks a fraction of edges and trains a small head to predict each masked edge's convexity and curve type from the encoder's post-message-passing face features. We combine MTM with a MoCo-style momentum-queue contrastive learning over B-rep-aware augmentations, a BFS-connected face-region masked-reconstruction objective, and pretraining on the ABC dataset and our new procedurally generated dataset to show strong performance on a number of benchmarks.

Problem

• parametric CAD
• boundary representation
• data-efficient learning
• self-supervised learning
• B-Rep

Innovation

• Masked Topology Modeling
• B-Rep
• Self-Supervised Learning
• Parametric CAD
• Contrastive Learning