BRepNet: A topological message passing system for solid models. (arXiv:2104.00706v1 [cs.LG])

Boundary representation (B-rep) models are the standard way 3D shapes are
described in Computer-Aided Design (CAD) applications. They combine lightweight
parametric curves and surfaces with topological information which connects the
geometric entities to describe manifolds. In this paper we introduce BRepNet, a
neural network architecture designed to operate directly on B-rep data
structures, avoiding the need to approximate the model as meshes or point
clouds. BRepNet defines convolutional kernels with respect to oriented coedges
in the data structure. In the neighborhood of each coedge, a small collection
of faces, edges and coedges can be identified and patterns in the feature
vectors from these entities detected by specific learnable parameters. In
addition, to encourage further deep learning research with B-reps, we publish
the Fusion 360 Gallery segmentation dataset. A collection of over 35,000 B-rep
models annotated with information about the modeling operations which created
each face. We demonstrate that BRepNet can segment these models with higher
accuracy than methods working on meshes, and point clouds.



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