Few-Shot B-rep Local Structure Retrieval with Structure-Aware Pretraining and Hard-Example Fine-Tuning

Existing B-rep retrieval methods primarily model whole-part similarity, whereas engineering reuse often requires finding complete models that contain a desired local structure. This setting is challenging because target structures can vary substantially in geometry and topology, especially when a few query examples are available. We present LocBrep, a few-shot local-structure retrieval framework for B-rep models. It couples structure-aware pretraining and hard-example fine-tuning with a coarse-to-fine retriever to learn transferable local representations, accurately retrieve specific parts, and support retrieval for CAD assemble. LocBrep first employs SmoothDiff pretraining, which combines multi-scale neighborhood consistency with topological positional regularization to obtain robust face-level representations. It then performs query-guided hard-example fine-tuning to adapt the encoder to a target structure from limited examples. For scalable retrieval, hyperedge-level coarse search recalls candidate parts, and a query-aware cross-graph reranker refines their ranking according to local structural correspondence. We further introduce LocBrep-Bench, comprising 4500 B-rep models from 50 local-structure categories with face-level annotations. LocBrep outperforms existing methods on FabWave and LocBrep-Bench under both zero-shot and one-shot settings, with up to a 15.71 percentage-point improvement in MAP. Experiments on Fusion 360 further validate its applicability to retrieval in CAD assemblies.