MeshWalker: Deep Mesh Understanding by Random Walks

Understand

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.