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.

翻译：随着几何深度学习技术的快速发展，许多基于网格的卷积算子被提出，用以桥接不规则网格结构与主流骨干网络。本文表明，尽管卷积操作具有其优势，但一种纯粹基于多层感知机（MLP）的简单架构已足以应对网格分类与语义分割任务。我们提出的新型网络架构Mesh-MLP，以配备热核特征（HKS）和二面角的网格顶点作为输入，用多层感知机（MLP）替代残差网络（ResNet）中的卷积模块，并采用层归一化（LN）对网络层进行归一化处理。该全MLP架构以端到端方式运行，且不包含池化模块。在网格分类与语义分割任务上的大量实验结果验证了该全MLP架构的有效性。