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arXiv · 2306.05246

A Task-driven Network for Mesh Classification and Semantic Part Segmentation

Qiujie Dong·Xiaoran Gong·Rui Xu·Zixiong Wang·Shuangmin Chen·Shiqing Xin·Changhe Tu·Wenping Wang
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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.

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Qiujie Dong, Xiaoran Gong, Rui Xu, Zixiong Wang, Shuangmin Chen, Shiqing Xin, Changhe Tu, Wenping Wang. 2023-06-08. A Task-driven Network for Mesh Classification and Semantic Part Segmentation. https://arxiv.org/abs/2306.05246

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