Directly transferring the 2D techniques to 3D scene generation is challenging due to significant resolution reduction and the scarcity of comprehensive real-world 3D scene datasets. To address these issues, our work introduces the Pyramid Discrete Diffusion model (PDD) for 3D scene generation. This novel approach employs a multi-scale model capable of progressively generating high-quality 3D scenes from coarse to fine. In this way, the PDD can generate high-quality scenes within limited resource constraints and does not require additional data sources. To the best of our knowledge, we are the first to adopt the simple but effective coarse-to-fine strategy for 3D large scene generation. Our experiments, covering both unconditional and conditional generation, have yielded impressive results, showcasing the model's effectiveness and robustness in generating realistic and detailed 3D scenes. Our code will be available to the public.

本文提出了金字塔离散扩散（PDD）模型，通过采用逐层生成的粗到精的多尺度模型，能够在资源限制下生成高质量的3D场景，而不需要额外的数据源。在无条件和有条件生成方面的实验表明，该模型在生成逼真而详细的3D场景方面表现出了很好的效果和鲁棒性。我们的代码将向公众开放。

金字塔扩散用于精细化的3D大场景生成