ll_brepnet (Implemented)

Implemented

ll_brepnet is no longer a roadmap item — it is built, trained, and documented. The package extracts the coedge graph + UV-grid geometry from STEP solids and trains a face-segmentation model end-to-end.

On the full official split of the Fusion 360 Gallery segmentation dataset (official 8 manufacturing-feature classes, 5,366-solid held-out test split) it reaches test mIoU ≈ 0.828 / accuracy ≈ 0.947 — exceeding the BRepNet paper’s reported ~0.65–0.72 mIoU, achieved with an MIT-clean architecture. A native-MLX port (parity-verified at 100% per-face agreement with PyTorch) runs the trained GNN on Apple Silicon.

See the package documentation:

• Overview — architecture, results, per-class IoU
• Installation
• Usage — prepare data, train, and segment STEP files

It is an independent, MIT-licensed implementation built on the toolkit’s own B-Rep machinery (cadling); inspired by BRepNet (arXiv:2104.00706) and UV-Net but containing no code from those projects.