DiffusionNet: Discretization Agnostic Learning on Surfaces

Understand

We introduce a new general-purpose approach to deep learning on 3D surfaces, based on the insight that a simple diffusion layer is highly effective for spatial communication.

• The resulting networks are automatically robust to changes in resolution and sampling of a surface -- a basic property which is crucial for practical applications.
• Our networks can be discretized on various geometric representations such as triangle meshes or point clouds, and can even be trained on one representation then applied to another.
• We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes.