Dense graph convolutional neural networks on 3D meshes for 3D object segmentation and classification

Abstract

This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh as a graph where each node corresponds to a face. To enhance the descriptive power of the graph, we introduce a 1-ring face neighborhood structure to derive novel multi-dimensional spatial and structure features to represent the graph nodes. Based on this new graph representation, we then design a densely connected graph convolutional block which aggregates local and regional features as the key construction component to build effective and efficient practical GCN models for 3D object classification and segmentation. We present experimental results to show that our new technique performs comparably to state of the art across a number of benchmark datasets where our models are also shown to have smaller number of parameters. We also present ablation studies to demonstrate the soundness of our design principles and the effectiveness of our practical models.