Masked Topology Modeling for Self-Supervised Learning on Parametric CAD

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.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2607.20642 [cs.CV]

(or arXiv:2607.20642v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.20642 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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