ExMeshCNN: An Explainable Convolutional Neural Network Architecture for 3D Shape Analysis

Triangular meshes have been actively used in computer graphics to represent 3D shapes. However, due to their non-uniform and irregular nature, learning such data with a Deep Neural Network is not straightforward. Transforming mesh data to simpler structures (e.g., voxel grids, point clouds, or multi-view 2D images) leads to other issues including spatial information loss and scalability. Traditional descriptors for mesh data simply extract hand-crafted features, which might not be effective in various environments. Several deep architectures that directly consume mesh data have been proposed, but their input features are still heuristic and unable to fully capture both geodesic and geometric characteristics of a mesh. In addition, their model architectures are not designed to be capable of providing visual explanations of their decision making.