BRepLift: B-Rep Reconstruction with Shared 3D Foundation Priors

B-Rep reconstruction requires recovering precise parametric geometry and valid topology from unstructured 3D observations. Existing surface-based methods typically pass task-specific predictions between reconstruction stages, compressing rich learned representations into intermediate outputs and creating a representational bottleneck. Greater geometric variation and topological complexity further amplify ambiguity and error propagation. We propose BRepLift, a framework that adapts pretrained 3D foundation features into B-Rep-aware representations shared across primitive identification, geometry recovery, and topology reasoning. Stage-specific designs exploit these features through clustering-guided face identification and context-conditioned geometry and topology prediction, capturing both intra- and inter-primitive information. Experiments on three test sets with complementary geometric and topological complexity demonstrate consistent improvements over DualBrep in face, edge, and vertex F1 scores and B-Rep validity. In particular, reconstruction validity increases from 10.60% to 22.14% on ABC-ParseNet and from 17.34% to 29.46% on our curated test set.