Deep generative models have shown success in generating 3D shapes with
different representations. In this work, we propose Neural Volumetric Mesh
Generator(NVMG) which can generate novel and high-quality volumetric meshes.
Unlike the previous 3D generative model for point cloud, voxel, and implicit
surface, the volumetric mesh representation is a ready-to-use representation in
industry with details on both the surface and interior. Generating this such
highly-structured data thus brings a significant challenge. We first propose a
diffusion-based generative model to tackle this problem by generating voxelized
shapes with close-to-reality outlines and structures. We can simply obtain a
tetrahedral mesh as a template with the voxelized shape. Further, we use a
voxel-conditional neural network to predict the smooth implicit surface
conditioned on the voxels, and progressively project the tetrahedral mesh to
the predicted surface under regularizations. The regularization terms are
carefully designed so that they can (1) get rid of the defects like flipping
and high distortion; (2) force the regularity of the interior and surface
structure during the deformation procedure for a high-quality final mesh. As
shown in the experiments, our pipeline can generate high-quality artifact-free
volumetric and surface meshes from random noise or a reference image without
any post-processing. Compared with the state-of-the-art voxel-to-mesh
deformation method, we show more robustness and better performance when taking
generated voxels as input.

本文提出了一种名为神经体维网格生成器的神经网络模型，可以从随机噪声或参考图像中生成高质量的三维网格结构，并且通过比较实验，证明了这种方法在使用体素作为输入时相对于其他最先进的方法更具有稳健性，并且拥有更好的性能。