Masked Topology Modeling for Self-Supervised Learning on Parametric CAD

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, a graph whose edge attributes the encoder can be asked to predict. 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 and pretrain on the ABC dataset and our new procedurally generated dataset to show strong performance on a number of benchmarks. We further demonstrate MTM's effectiveness across B-rep architectures.