Corpus ID: 233880804

DiffusionNet: Discretization Agnostic Learning on Surfaces

  title={DiffusionNet: Discretization Agnostic Learning on Surfaces},
  author={Nicholas Sharp and Souhaib Attaiki and Keenan Crane and Maks Ovsjanikov},
We introduce a new 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 automatically generalize across different samplings and resolutions 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… Expand
An introduction to deep learning on meshes
This course aims to take a deep dive into the discrete mesh representation, the most popular representation for shapes in computer graphics and provides different ways of covering aspects of deep learning on meshes for the virtual audience. Expand
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes
The analysis of deforming 3D surface meshes is accelerated by autoencoders since the lowdimensional embeddings can be used to visualize underlying dynamics. But, state-of-the-art mesh convolutionalExpand