This paper presents a novel latent 3D diffusion model for the generation of
neural voxel fields, aiming to achieve accurate part-aware structures. Compared
to existing methods, there are two key designs to ensure high-quality and
accurate part-aware generation. On one hand, we introduce a latent 3D diffusion
process for neural voxel fields, enabling generation at significantly higher
resolutions that can accurately capture rich textural and geometric details. On
the other hand, a part-aware shape decoder is introduced to integrate the part
codes into the neural voxel fields, guiding the accurate part decomposition and
producing high-quality rendering results. Through extensive experimentation and
comparisons with state-of-the-art methods, we evaluate our approach across four
different classes of data. The results demonstrate the superior generative
capabilities of our proposed method in part-aware shape generation,
outperforming existing state-of-the-art methods.

本文提出了一种新颖的潜在的 3D 扩散模型来生成神经体素场，旨在实现准确的部件感知结构。通过对现有方法的比较，我们采用了两个关键设计来确保高质量和准确的部件感知生成。一方面，我们引入了潜在的 3D 扩散过程来生成神经体素场，使其能够以显著更高的分辨率生成富有纹理和几何细节。另一方面，我们引入了部件感知的形状解码器，将部件代码整合到神经体素场中，引导准确的部件分解并产生高质量的渲染结果。通过广泛实验和与现有最先进方法的比较，我们对我们的方法在四种不同类别的数据上进行了评估。结果表明，我们提出的方法在部件感知形状生成方面具有优秀的生成能力，优于现有最先进方法。