论文标题
TETGAN：四面体网状的卷积神经网络
TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
论文作者
论文摘要
我们提出了Tetgan，这是一种卷积神经网络，旨在生成四面体网。我们使用不规则的四面体网格代表形状，该网格编码占用和位移场。我们的公式可以定义四面体卷积，汇总和上采样操作，以将明确的网格连接与可变拓扑属合成。提出的神经网络层学习每个四面体上的深度特征，并学会在多个尺度上提取空间区域内的模式。我们说明了我们技术将四面体网格编码为语义上有意义的潜在空间的功能，该空间可用于形状编辑和合成。我们的项目页面位于https://threedle.github.io/tetgan/。
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/.
