Pyramid Diffusion for Fine 3D Large Scene Generation
Yuheng Liu Xinke Li Xueting Li Lu Qi Chongshou Li Ming-Hsuan Yang
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.