MeT: mesh transformer with an edge

Transformers have been widely applied in various vision tasks processing different data, such as images, videos and point clouds. However, the use of transformers in 3D mesh analysis remains largely unexplored. To address this gap, we propose a mesh transformer (MeT) that utilizes local self-attention on edges. MeT is based on a transformer layer that uses vector attention for edges, which is a kind of attention operator that supports adaptive modulation to both feature vectors and individual feature channels. Based on the transformer block, we build a lightweight mesh transformer network that consists of encoder and decoder. MeT provides general backbones for subsequent 3D mesh analysis tasks. To evaluate the effectiveness of our network MeT, we conduct experiments on two classic mesh analysis tasks: shape classification and shape segmentation. MeT achieves the state-of-the-art performance on multiple datasets for two tasks. We also conduct ablation studies to show the effectiveness of key designs in our network.