TPNet: A novel mesh analysis method via topology preservation and perception enhancement

Abstract

3D polygon mesh is an important and popular representation of 3D shapes in the field of computer graphics and computer-aided design. Recent works have introduced deep neural networks for mesh data analysis. However, it remains a great challenge for convolutional neural networks to learn mesh shape effectively due to the difficulty of deriving long-range information from the irregular data structure. Another tricky problem is how to explore a high effective representation of the input feature for mesh learning network. In this paper, we propose a novel 3D mesh learning method, named TPNet, which enhances the perception and learns long-range information by customizing dilated convolution for non-uniform mesh data. Specifically, we devise a faithful strategy that accurately locates the position where dilated convolutions can be adopted, despite the non-uniformity and irregularity of the mesh data. Furthermore, our method proposes a topology-preserved 7-dimension feature representation for mesh data and aggregates the features via stacks of convolution layers and dilated convolution layers. Extensive experiments demonstrate the effectiveness of our approach on 3D mesh learning tasks, where we show superior or at least comparable performance to the SOTA approaches.