BRepNet | topological message passing system for solid models

BRepNet is a Python library. BRepNet has no bugs, it has no vulnerabilities and it has low support. However BRepNet build file is not available and it has a Non-SPDX License. You can download it from GitHub.

BRepNet is a neural network specifically designed to operate on solid models. It uses additional topological information present in the boundary representation (B-Rep) data structure to perform convolutions in a way which is not possible for arbitrary graphs. As B-Reps describe manifolds, they contain additional topological information which includes the ordering of edges around faces as well as the face adjacency. The topology is defined using oriented edges called coedges. Each coedge maintains an adjacency relationship with the next and previous coedge around its parent face, the mating coedge on the adjacent face, the parent face and the parent edge. Using this information, we can identify faces, edges and coedges in the neighborhood of some starting coedge (red), using topological walks. A topological walk is a series of instructions we move us from the starting coedge to a nearby entity. In the figure above (B) the we show a walk from the red starting coedge to its mating coedge, to the next coedge in the loop, to mating coedge and finally to the parent face. Using multiple topological walks we can define a group of entities in the neighborhood of the starting coedge. The instructions which define the neighboring entities are marked in the figure (C). The BRepNet implementation allows you to define any group of entities using a kernel file. See here for an example of a kernel file for kernel entities shown above. The BRepNet convolution algorithm concatenates feature vectors from the entities defined in the kernel file relative to the starting coedge (red). The resulting vector is passed through an MLP and the output becomes the hidden state for this coedge in the next network layer. The procedure is repeated for each coedge in the model, then new hidden state vectors for the faces and edges are generated by pooling the coedge hidden states onto their parent faces and edges. See the paper for more details. The actual implementation of the BRepNet convolution can been seen in the BRepNetLayer.forward() method.