Masked BRep Autoencoder via Hierarchical Graph Transformer

We introduce a novel self-supervised learning framework thatautomatically learns representations from input computer-aided design(CAD) models for downstream tasks, including part classification, mod-eling segmentation, and machining feature recognition. To train our net-work, we construct a large-scale, unlabeled dataset of boundary repre-sentation (BRep) models. The success of our algorithm relies on two keycomponents. The first is a masked graph autoencoder that reconstructsrandomly masked geometries and attributes of BReps for representa-tion learning to enhance the generalization. The second is a hierarchi-cal graph Transformer architecture that elegantly fuses global and locallearning by a cross-scale mutual attention block to model long-range ge-ometric dependencies and a graph neural network block to aggregatelocal topological information. After training the autoencoder, we replaceits decoder with a task-specific network trained on a small amount oflabeled data for downstream tasks. We conduct experiments on varioustasks and achieve high performance, even with a small amount of labeleddata, demonstrating the practicality and generalizability of our model.Compared to other methods, our model performs significantly better ondownstream tasks with the same amount of training data, particularlywhen the training data is very limited.