MidSurfNet: Learning Face Pairing for Mid-surface Abstraction of Thin-walled CAD Models

Abstract:Mid-surface abstraction is an important preprocessing step for finite element analysis of thin-walled CAD models, and face pairing is its central subproblem. Existing face-pairing methods rely on handcrafted geometric criteria whose thresholds are hard to tune when a model has multiple local wall thicknesses; their groupings depend on threshold settings and processing order, so the same model can yield inconsistent results. We present MidSurfNet, a learning-based face-pairing method that couples a learned face-pair scorer with a deterministic face-group composition. The scorer evaluates every unordered face pair with two separately learned evidence streams: a geometry stream combining continuous pairing criteria with a conditional shape correction, and an attributed-topology stream over the B-Rep face-adjacency graph. A pair-conditioned gate fuses the two streams, and independent per-pair decisions retain opposing-face support relations at one operating threshold selected once on validation data, replacing rather than adding to the per-model thresholds of rule-based pipelines. Under a connected-and-bipartite condition, the composition stage organizes the retained relations into variable-cardinality m-to-n face groups, each independent of processing order for a fixed support graph and unique up to its two side labels. We also construct the MidSurf dataset, a benchmark of 1,575 manually annotated CAD models. On the test set, MidSurfNet attains a pair-level F1-Score of 87.32%, 23.22 percentage points above the strongest rule-based baseline, and an end-to-end Completion Rate of 75.42%, including 61.90% on the multi-wall-thickness category the evaluated rule-based implementations do not support. We demonstrate practical utility by generating mid-surfaces from the composed face groups through an industrial mid-surface API and running finite element analyses on the resulting shell models.

Graphics (cs.GR); Machine Learning (cs.LG)

arXiv:2606.01891 [cs.GR]

(or arXiv:2606.01891v2 [cs.GR] for this version)

https://doi.org/10.48550/arXiv.2606.01891 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.