基于Transformer的三角形网格分类分割网络

Classification and segmentation network based on Transformer for triangular mesh

三角形网格是一种重要的几何数据结构,能有效地表达三维模型的形状细节,但三角形网格面元素的分布并不规则,因此将现有的深度神经网络直接应用到网格上较为困难。针对三角形网格不规则的结构问题,直接将网格的面作为Token,提出一种将Transformer应用于三角形网格的深度神经网络。首先,将面的重心坐标或谱域特征作为位置信息,融合其内蕴特征作为输入特征,并对输入特征位置嵌入;其次,利用自注意力模块提取全局特征,利用面卷积模块提取局部特征,以增强网络局部特征的提取能力;最后,融合局部特征和全局特征构建应用于三角形网格上的分类和分割深度神经网络。在SHREC分类数据集和COSEG分割数据集上的实验结果表明,该方法准确率较高且可以有效地提升训练速度。

Triangular mesh is an important geometric data structure for effectively expressing the shape details of 3D models.However,the irregular distribution of surface elements poses a challenge in directly applying existing neural networks to triangular meshes.To address the irregular structure of triangular meshes,taking the mesh surface as Token directly,a deep neural network based on Transformer for triangular meshes is proposed.Firstly,the coordinates for the center of gravity or spectral domain features of the face are utilized as the position information,incorporating its intrinsic features as the input feature,and followed by the position embedding of the input feature.Secondly,the global feature is extracted through a self-attention module,and a face convolution module was employed to extract local features,thereby enhancing the ability to extract local features.Finally,integrating the local and global features,the classification and segmentation deep neural network for triangular meshes is constructed.The experimental results on the SHREC classification dataset and COSEG segmentation dataset demonstrate the proposed method’s high accuracy and its effectiveness in improving the training speed.