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
this https URL

本文提出了一种扩展离散扩散模型的方法，用于生成 3D 场景，并在场景级别上使用分类分布来指定多个对象。同时，使用潜在扩散模型可以降低训练和部署的计算成本。作者还使用该模型进行了场景完成的任务，并在实验中证明了该模型生成的场景的合理性和任务完成的优越性。