Segmenting 3D shapes represented by meshes remains a challenging problem, due to the irregularity and complexity of meshes. Point cloud, on the other hand, can be considered as the simplest no-frills approximation for meshes. Therefore, in this paper, we regard the shape segmentation problem as a point labeling task: Given a shape, we first transform it into points encoding barycenters and normal vectors of faces. Then we construct a Barycentric Dual Graph (BDG) on the transformed points, and propose a Barycentric Dual Graph Edge Convolution (BDGEC) to extract features from the graph. Based on the BDGEC, we further propose a novel point-based deep neural network (DNN) named local-global combined point-based network (LGCPNet). Our LGCPNet consists of three modules, of which the Local Module and Global Module capture local and global features respectively, while the Fusion Module uses a gate mechanism to aggregate local features and global features, and obtain the point labeling result. Comprehensive experimental results on various datasets demonstrate that the proposed network inherits the merits of point-based DNNs and achieves the state-of-the-art performance.

由于网格的不规则性和复杂性，分割以网格表示的3D形状仍然是一个具有挑战性的问题。另一方面，点云可以被认为是网格最简单的无褶边近似。因此，在本文中，我们将形状分割问题视为点标记任务：给定形状，我们首先将其转换为编码人的重心和法线向量的点。然后，在变换后的点上构造重心对偶图（BDG），并提出重心对偶图边缘卷积（BDGEC）以从图中提取特征。在BDGEC的基础上，我们进一步提出了一种新颖的基于点的深度神经网络（DNN），称为局部全局组合基于点的网络（LGCPNet）。我们的LGCPNet包含三个模块，其中“本地模块”和“全局模块”分别捕获本地和全局特征，而“融合模块”使用门机制汇总本地特征和全局特征，并获得点标记结果。在各种数据集上的综合实验结果表明，所提出的网络继承了基于点的DNN的优点，并实现了最新的性能。