AI RESEARCH
STEP-Parts: Geometric Partitioning of Boundary Representations for Large-Scale CAD Processing
arXiv CS.AI
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ArXi:2604.14927v1 Announce Type: cross Many CAD learning pipelines discretize Boundary Representations (B-Reps) into triangle meshes, discarding analytic surface structure and topological adjacency and thereby weakening consistent instance-level analysis. We present STEP-Parts, a deterministic CAD-to-supervision toolchain that extracts geometric instance partitions directly from raw STEP B-Reps and transfers them to tessellated carriers through retained source-face correspondence, yielding instance labels and metadata for downstream learning and evaluation.