Diffusion models have emerged as the state-of-the-art for image generation,
among other tasks. Here, we present an efficient diffusion-based model for
3D-aware generation of neural fields. Our approach pre-processes training data,
such as ShapeNet meshes, by converting them to continuous occupancy fields and
factoring them into a set of axis-aligned triplane feature representations.
Thus, our 3D training scenes are all represented by 2D feature planes, and we
can directly train existing 2D diffusion models on these representations to
generate 3D neural fields with high quality and diversity, outperforming
alternative approaches to 3D-aware generation. Our approach requires essential
modifications to existing triplane factorization pipelines to make the
resulting features easy to learn for the diffusion model. We demonstrate
state-of-the-art results on 3D generation on several object classes from
ShapeNet.

本研究提出了一种基于扩散模型的高效的神经场三维生成方法，通过将训练数据转换为连续占据场并将其分解为一组轴对齐的三面体特征表示来实现，训练过程中，采用现有的二维扩散模型对这些特征表示进行训练，生成高质量和多样性的三维神经场，且优于其他三维生成方法。