As an important type of geometric data for 3D shapes, the unique topological connection makes the mesh more powerful than other types of data, but it also introduces complexity and irregularity. In this paper, we propose a Topological Perception Network (TPN) that consumes meshes directly to learn 3D shape representation via informative topology property. More specifically, to tackle the complexity and irregularity problem, a Topological Perception Attention (TPA) is designed that could incorporate local topological information efficiently via focusing on more important edges of the local topological neighborhood. Meanwhile, it could be stacked to produce global shape representation. Compared with the state-of-the-art, the proposed TPN uses less than half of the vertex number to get better performance, while costing less memory and computational time. Experiments on Model-Net40 and ShapeNet Core55 datasets demonstrate the effectiveness of our method on classification and retrieval.

This study is published in 2020 IEEE International Conference on Image Processing (ICIP 2020).