Most attempts to represent 3D shapes for deep learning have focused on
volumetric grids, multi-view images and point clouds. In this paper we look at
the most popular representation of 3D shapes in computer graphics - a
triangular mesh - and ask how it can be utilized within deep learning. The few
attempts to answer this question propose to adapt convolutions & pooling to
suit Convolutional Neural Networks (CNNs). This paper proposes a very different
approach, termed MeshWalker, to learn the shape directly from a given mesh. The
key idea is to represent the mesh by random walks along the surface, which
"explore" the mesh's geometry and topology. Each walk is organized as a list of
vertices, which in some manner imposes regularity on the mesh. The walk is fed
into a Recurrent Neural Network (RNN) that "remembers" the history of the walk.
We show that our approach achieves state-of-the-art results for two fundamental
shape analysis tasks: shape classification and semantic segmentation.
Furthermore, even a very small number of examples suffices for learning. This
is highly important, since large datasets of meshes are difficult to acquire.

本文提出了一种新方法 MeshWalker，利用随机游走算法，在三角网格结构的 3D 形状中探索和学习几何和拓扑特征。该方法在形状分类和语义分割方面表现良好，并且仅需要很少量的样本即可进行学习。