3D models are widely used in various industries, and mesh data has become an indispensable part of 3D modeling because of its unique advantages. Mesh data can provide an intuitive and practical expression of rich 3D information. However, its disordered, irregular data structure and complex surface information make it challenging to apply with deep learning models directly. Traditional mesh data processing methods often rely on mesh models with many limitations, such as manifold, which restrict their application scopes in reality and do not fully utilize the advantages of mesh models. This paper proposes a novel end-to-end framework for addressing the challenges associated with deep learning in mesh models centered around graph neural networks (GNN) and is titled InfoGNN. InfoGNN treats the mesh model as a graph, which enables it to handle irregular mesh data efficiently. Moreover, we propose InfoConv and InfoMP modules, which utilize the position information of the points and fully use the static information such as face normals, dihedral angles, and dynamic global feature information to fully use all kinds of data. In addition, InfoGNN is an end-to-end framework, and we simplify the network design to make it more efficient, paving the way for efficient deep learning of complex 3D models. We conducted experiments on several publicly available datasets, and the results show that InfoGNN achieves excellent performance in mesh classification and segmentation tasks.

翻译：三维模型在各行业广泛应用，网格数据因其独特优势已成为三维建模不可或缺的组成部分。网格数据能够直观且实用地表达丰富的三维信息。然而，其无序、不规则的数据结构与复杂的表面信息，使其难以直接应用于深度学习模型。传统的网格数据处理方法通常依赖于具有诸多限制的网格模型（如流形约束），这不仅限制了其实际应用范围，也未充分发挥网格模型的优势。本文提出一种新颖的端到端框架——InfoGNN，旨在解决以图神经网络为核心的网格模型深度学习所面临的挑战。InfoGNN将网格模型视为图结构，从而能高效处理不规则网格数据。此外，我们提出了InfoConv与InfoMP模块，这些模块利用点的位置信息，并充分融合面法向量、二面角等静态信息以及动态全局特征信息，以实现对各类数据的全面利用。同时，InfoGNN作为端到端框架，通过简化网络设计提升了计算效率，为复杂三维模型的高效深度学习开辟了新途径。我们在多个公开数据集上进行了实验，结果表明InfoGNN在网格分类与分割任务中均取得了优异性能。