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 neighbourhood 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 will present experimental results to show that our new technique outperforms state of the art where our models are shown to have the smallest number of parameters and consietently achieve the highest accuracies across a number of benchmark datasets. We will also present ablation studies to demonstrate the soundness of our design principles and the effectiveness of our practical models.

中文翻译：

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用于 3D 对象分割和分类的 3D 网格上的密集图卷积神经网络

本文介绍了用于 3D 对象分割和分类的 3D 网格上的图卷积神经网络 (GCN) 的新设计。我们使用网格的面作为基本处理单元，并将 3D 网格表示为一个图形，其中每个节点对应一个面。为了增强图的描述能力，我们引入了一个 1 环面邻域结构来推导新颖的多维空间和结构特征来表示图节点。基于这种新的图表示，我们然后设计了一个密集连接的图卷积块，它聚合局部和区域特征作为关键构建组件，以构建有效且高效的实用 GCN 模型，用于 3D 对象分类和分割。我们将展示实验结果，以表明我们的新技术优于现有技术，其中我们的模型被证明具有最少数量的参数，并在许多基准数据集上始终达到最高准确度。我们还将展示消融研究，以证明我们设计原则的合理性和我们实用模型的有效性。