MeshNet: Mesh Neural Network for 3D Shape Representation

TL;DR

MeshNet is a novel neural network architecture designed to effectively learn 3D shape representations directly from mesh data, overcoming the challenges of complexity and irregularity in mesh structures.

Contribution

The paper introduces MeshNet, a new architecture with face-unit and feature splitting, enabling better 3D shape representation from mesh data compared to prior methods.

Findings

Achieves competitive 3D shape classification accuracy

Demonstrates effective retrieval performance

Outperforms existing methods on benchmark datasets

Abstract

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.