BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding
Summary
BRepCLIP introduces contrastive multimodal pretraining on boundary representation (BRep) primitives for CAD understanding, aligning BRep geometry with language and image embeddings to achieve state-of-the-art retrieval and zero-shot classification.
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Paper page - BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding
Source: https://huggingface.co/papers/2606.05515
Abstract
BRepCLIP enables multimodal representation learning for CAD models by aligning boundary representation geometry with language and image embeddings through contrastive pretraining, achieving superior retrieval and classification performance compared to point-based methods.
Learning representations ofCAD modelsis a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD -boundary representationsBReps, which encodes exact parametric surfaces, curves, and their topology, has received little attention as a representation learning substrate. We introduce BRepCLIP, the first framework to align BRep geometry with language and image embeddings throughcontrastive pretraining. We model each CAD object as a sequence of face andedge tokenswith separatediscrete vocabulariesfor surface and curve geometry, augmented with spatial andsemantic descriptorsthat capture surface types (e.g., cylindrical, torus, NURBS) and curve primitives (e.g., line, arc, B-spline). Atransformer encoderaggregates these tokens into aglobal BRep embedding, aligned withCLIP’s text and image encoders via a joint contrastive objective. BRepCLIPgenerates more discriminative and semantically grounded embeddings than existing point-based alternatives, improvingTop-1 retrievalover OpenShape by 40.4%, 22.0%, and 23.9% on ABC, CADParser, and Automate, respectively, and improvingzero-shot classificationon FabWave by 15% in Top-1 score. We further demonstrate its utility as a CAD-aware similarity metric for evaluating text and image-conditioned CAD generation, establishing the importance of structure-aware pretraining formultimodal CAD understanding. Project page is available at https://muhammadusama100.github.io/BrepClip2026/
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