Masked Topology Modeling for Self-Supervised Learning on Parametric CAD
The paper introduces Masked Topology Modeling (MTM), a self-supervised pretraining task that leverages the face-adjacency graph unique to B-Reps. By masking edges and predicting their convexity and curve type, combined with MoCo contrastive learning and BFS-connected region reconstruction, the method achieves strong performance on several benchmarks after pretraining on ABC and a new procedural dataset.
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[Submitted on 22 Jul 2026]
Title:Masked Topology Modeling for Self-Supervised Learning on Parametric CAD
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.20642 [cs.CV]
(or arXiv:2607.20642v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.20642
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Heinrich Jiang [view email] [v1] Wed, 22 Jul 2026 18:13:08 UTC (546 KB)
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