Classification and segmentation network based on Transformer for triangular mesh
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.
geometry deep learningTransformertriangular mesh3D model classification3D model segmentation
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NSTL
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