TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
This work addresses mesh generation for 3D modeling and simulation, offering a novel method for handling irregular grids, but it appears incremental as it adapts existing CNN concepts to tetrahedral domains.
The authors tackled the problem of generating tetrahedral meshes by introducing TetGAN, a convolutional neural network that learns to synthesize explicit mesh connectivity with variable topological genus, enabling shape editing and synthesis through a semantically meaningful latent-space.
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes. We represent shapes using an irregular tetrahedral grid which encodes an occupancy and displacement field. Our formulation enables defining tetrahedral convolution, pooling, and upsampling operations to synthesize explicit mesh connectivity with variable topological genus. The proposed neural network layers learn deep features over each tetrahedron and learn to extract patterns within spatial regions across multiple scales. We illustrate the capabilities of our technique to encode tetrahedral meshes into a semantically meaningful latent-space which can be used for shape editing and synthesis. Our project page is at https://threedle.github.io/tetGAN/.