BRepPreDiff: Diffusion-Based Self-Supervised Representation Learning for Boundary Representations

Diffusion models have recently demonstrated strong potential for generating structured boundary representation (B-Rep) models by modeling complex geometric distributions and dependencies. However, their use for transferable B-Rep representation learning remains largely unexplored. Meanwhile, large-scale Computer-Aided Design (CAD) datasets are predominantly unlabeled, and models stored in intermediate exchange formats typically lack their original modeling histories, making scalable semantic representation learning from CAD data particularly challenging. To address this problem, we introduce $\text{BRepPreDiff}$, a diffusion-based self-supervised pre-training framework for learning transferable B-Rep representations across downstream tasks, including face-level semantic segmentation and shape classification. BRepPreDiff consists of a $\text{topology-aware B-Rep encoder}$ and a $\text{diffusion-based pre-training}$ strategy. The encoder employs edge-update attention to explicitly model geometric and topological interactions between adjacent B-Rep faces and their relations. During pre-training, we preserve the B-Rep topology while corrupting geometric features at sampled noise levels and train the encoder to predict the injected noise and reconstruct clean geometric features. This multi-level denoising objective enables the model to learn transferable geometric and structural features from unlabeled B-Rep data. For downstream adaptation, the pretrained encoder is jointly fine-tuned with lightweight MLP heads for face-level semantic segmentation and whole-model classification. BRepPreDiff achieves $86.64\%$ mIoU on Fusion360Seg and $86.38\%$ classification accuracy on TMCAD, exceeding the strongest compared baselines by 5.05 and 2.28 percentage points, respectively. We further introduce $\text{BLEND}$, a CAD dataset comprising 18K B-Rep models with face-level blend annotations, providing a large-scale benchmark for blend-face recognition and semantic supervision for design-intent recovery and feature-aware CAD analysis.