Bahareh Shakibajahromi, Saeed Shayestehmanesh, Daniel Schwartz and Ali Shokoufandeh

Mesh is a preeminent and efficient data structure for 3D objects that support high-resolution representation. Recent deep learning techniques applied to unstructured mesh data define rigid convolutions that fail to capture the rich geometric and topological attributes of the mesh. We propose an efficient, deterministic process that converts a mesh into a hybrid graph and captures the geometric features of its constituting components: vertices, edges, and faces. In addition, we introduce a novel representation learning framework that encodes mesh elements by focusing on the most relevant parts of the geometric structure using a dual-level attention architecture. We evaluate the efficacy of the proposed representation in the context of the 3D shape segmentation problem. The superior performance of the proposed representation to the state of the art in supervised segmentation illustrates the soundness of the proposed attention model.