TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
William M Gao (University of Chicago),* April Wang (Threedle), Gal Metzer (Tel Aviv University), Raymond A Yeh (Toyota Technological Institute at Chicago ), Rana Hanocka (University of Chicago)
The 33rd British Machine Vision Conference
Abstract
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/.Video
Citation
@inproceedings{Gao_2022_BMVC,
author = {William M Gao and April Wang and Gal Metzer and Raymond A Yeh and Rana Hanocka},
title = {TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0365.pdf}
}
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