MeshWalker: Deep Mesh Understanding by Random Walks

9 Jun 2020  ·  Alon Lahav, Ayellet Tal ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Object Recognition Cube Engraving MeshWalker (ours) Accuracy 98.6 # 1
3D Object Recognition ModelNet40 MeshWalker (ours) Accuracy 92.3% # 2
3D Object Recognition SHREC11, Split10-10 MeshWalker (ours) Per-Class Accuracy 97.1 # 1
3D Object Recognition SHREC11, Split16-4 MeshWalker (ours) Per-Class Accuracy 98.6 # 1


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