MeshNet: Mesh Neural Network for 3D Shape Representation
This addresses the challenge of representing 3D shapes for computer vision and graphics applications, but it is incremental as it focuses on an underutilized data type rather than a new paradigm.
The paper tackles the problem of 3D shape representation using mesh data, which is complex and irregular, by proposing MeshNet, a mesh neural network that achieves satisfying performance in 3D shape classification and retrieval compared to state-of-the-art methods.
Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.