BRepCLIP：針對CAD理解的BRep基本體上的對比多模態預訓練

摘要

學習CAD模型的表徵是一個很大程度上尚未解決的問題。儘管3D表徵學習在點雲和網格領域蓬勃發展，但CAD的原生格式——邊界表示（BReps），它編碼了精確的參數化曲面、曲線及其拓撲結構——作為表徵學習的基礎卻鮮少受到關注。我們引入了BRepCLIP，這是首個通過對比預訓練將BRep幾何與語言和圖像嵌入對齊的框架。我們將每個CAD物件建模為一個由面和邊標記組成的序列，分別為曲面和曲線幾何設置獨立的離散詞彙表，並輔以空間和語義描述符，用於捕捉曲面類型（例如圓柱面、環面、NURBS）和曲線基元（例如直線、弧線、B樣條曲線）。一個Transformer編碼器將這些標記聚合為全局BRep嵌入，並通過聯合對比目標與CLIP的文字和圖像編碼器對齊。BRepCLIP生成的嵌入比現有的基於點的方法更具判別性和語義基礎，在ABC、CADParser和Automate數據集上，相較於OpenShape，Top-1檢索分別提升了40.4%、22.0%和23.9%，並在FabWave上的零樣本分類中，Top-1分數提升了15%。我們進一步展示了其作為CAD感知相似度度量在評估文本和圖像條件下的CAD生成中的效用，證明了結構感知預訓練對於多模態CAD理解的重要性。專案頁面可於 https://muhammadusama100.github.io/BrepClip2026/ 查看。

English

Learning representations of CAD models is a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD - boundary representations BReps, 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 through contrastive pretraining. We model each CAD object as a sequence of face and edge tokens with separate discrete vocabularies for surface and curve geometry, augmented with spatial and semantic descriptors that capture surface types (e.g., cylindrical, torus, NURBS) and curve primitives (e.g., line, arc, B-spline). A transformer encoder aggregates these tokens into a global BRep embedding, aligned with CLIP's text and image encoders via a joint contrastive objective. BRepCLIP generates more discriminative and semantically grounded embeddings than existing point-based alternatives, improving Top-1 retrieval over OpenShape by 40.4%, 22.0%, and 23.9% on ABC, CADParser, and Automate, respectively, and improving zero-shot classification on 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 for multimodal CAD understanding. Project page is available at https://muhammadusama100.github.io/BrepClip2026/