BRepFacetGen: Reverse Engineering B-Reps By Generative Face Segmentation

Boundary representations (B-Reps) are fundamental to com-puter aided design (CAD), as they encode editable parametric surfacestogether with the precise topological structure required by downstreamengineering workflows. In contrast, modern 3D generative models pro-duce meshes or implicit fields, leaving a gap between high-quality geom-etry synthesis and CAD-native representations. Existing reconstructionpipelines treat B-Rep recovery as deterministic segmentation and fit-ting on discrete samples, despite the inherent ambiguity of CAD-styleface decompositions, often resulting in unstable topology recovery. Weintroduce BRepFacetGen, a generative framework that formulates CADstructure inference as a latent variable problem conditioned on geometryrepresenting continuous surfaces. Our representation couples a pretrainedgeometry latent set with a geometry-conditioned label latent space thatmodels multiple plausible dense surface segmentations, naturally han-dling permutation ambiguity and structural non-uniqueness. Joint de-coding yields a faceted mesh where each segment maps to a B-Rep facethat can be recovered by surface fitting and topology reconstruction. Ex-periments show improved segmentation and more reliable downstreamB-Rep recovery, as well as strong performance in image-conditioned andpoint-conditioned B-Rep generation.