Partial Shape Recognition of Boundary-Representation Models using Deep Neural Networks.

Abstract

Shape recognition of the 3D shapes in computer-aided design (CAD) can help automate the product modelling and reduce the time to develop a new product. In this paper, in order to recognize partial shapes of 3D CAD models such as bolts and fillets, we propose a shape recognition method based on graph-neural-networks, which learns geometrical and topological information from data of annotated 3D CAD models. By using the graph of face adjacencies of the BRep model as input to the learning model, it is possible to predict recognition target directly from the BRep models. We applied this method to machine part data and verified its effectiveness. We demonstrate that our method can classify the BRep models in FabWave dataset with higher accuracy than the methods based on the point clouds.