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- W4380136762 abstract "With the rapid development of geometric deep learning techniques, many mesh-based convolutional operators have been proposed to bridge irregular mesh structures and popular backbone networks. In this paper, we show that while convolutions are helpful, a simple architecture based exclusively on multi-layer perceptrons (MLPs) is competent enough to deal with mesh classification and semantic segmentation. Our new network architecture, named Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and dihedral angles as the input, replaces the convolution module of a ResNet with Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform the normalization of the layers. The all-MLP architecture operates in an end-to-end fashion and does not include a pooling module. Extensive experimental results on the mesh classification/segmentation tasks validate the effectiveness of the all-MLP architecture." @default.
- W4380136762 created "2023-06-10" @default.
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- W4380136762 date "2023-06-08" @default.
- W4380136762 modified "2023-10-12" @default.
- W4380136762 title "Mesh-MLP: An all-MLP Architecture for Mesh Classification and Semantic Segmentation" @default.
- W4380136762 doi "https://doi.org/10.48550/arxiv.2306.05246" @default.
- W4380136762 hasPublicationYear "2023" @default.
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