We develop a generalized 3D shape generation prior model, tailored for
multiple 3D tasks including unconditional shape generation, point cloud
completion, and cross-modality shape generation, etc. On one hand, to precisely
capture local fine detailed shape information, a vector quantized variational
autoencoder (VQ-VAE) is utilized to index local geometry from a compactly
learned codebook based on a broad set of task training data. On the other hand,
a discrete diffusion generator is introduced to model the inherent structural
dependencies among different tokens. In the meantime, a multi-frequency fusion
module (MFM) is developed to suppress high-frequency shape feature
fluctuations, guided by multi-frequency contextual information. The above
designs jointly equip our proposed 3D shape prior model with high-fidelity,
diverse features as well as the capability of cross-modality alignment, and
extensive experiments have demonstrated superior performances on various 3D
shape generation tasks.

本篇论文提出了一个通用的 3D 形状模型生成模型，采用向量量化变分自编码器、离散扩散生成器和多频率融合模块等技术，实现了高保真度、多样性以及跨模态对齐等功能，能够应用在点云补全和多种 3D 形状生成任务中，实验证明其在各种任务中都表现出较高的性能。