  In this paper, we learn a diffusion model to generate 3D data on a
scene-scale. Specifically, our model crafts a 3D scene consisting of multiple
objects, while recent diffusion research has focused on a single object. To
realize our goal, we represent a scene with discrete class labels, i.e.,
categorical distribution, to assign multiple objects into semantic categories.
Thus, we extend discrete diffusion models to learn scene-scale categorical
distributions. In addition, we validate that a latent diffusion model can
reduce computation costs for training and deploying. To the best of our
knowledge, our work is the first to apply discrete and latent diffusion for 3D
categorical data on a scene-scale. We further propose to perform semantic scene
completion (SSC) by learning a conditional distribution using our diffusion
model, where the condition is a partial observation in a sparse point cloud. In
experiments, we empirically show that our diffusion models not only generate
reasonable scenes, but also perform the scene completion task better than a
discriminative model. Our code and models are available at
https://github.com/zoomin-lee/scene-scale-diffusion
