We present XCube (abbreviated as $\mathcal{X}^3$), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to $1024^3$ in a feed-forward fashion without time-consuming test-time optimization. To achieve this, we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure. Apart from generating high-resolution objects, we demonstrate the effectiveness of XCube on large outdoor scenes at scales of 100m$\times$100m with a voxel size as small as 10cm. We observe clear qualitative and quantitative improvements over past approaches. In addition to unconditional generation, we show that our model can be used to solve a variety of tasks such as user-guided editing, scene completion from a single scan, and text-to-3D. The source code and more results can be found at https://research.nvidia.com/labs/toronto-ai/xcube/.

翻译：我们提出了XCube（缩写为$\mathcal{X}^3$），一种用于具有任意属性的高分辨率稀疏三维体素网格的新型生成模型。我们的模型能够以前馈方式生成数百万个体素，其最精细有效分辨率可达$1024^3$，无需耗时的测试时优化。为实现这一目标，我们采用了一种分层体素潜在扩散模型，该模型基于高效的VDB数据结构构建的自定义框架，以从粗到细的方式逐步生成更高分辨率的网格。除了生成高分辨率物体外，我们还展示了XCube在100m$\times$100m尺度、体素尺寸小至10cm的大型室外场景上的有效性。我们观察到相较于以往方法在定性和定量上均有明显提升。除了无条件生成，我们还展示了该模型可用于解决多种任务，例如用户引导编辑、单次扫描的场景补全以及文本到三维生成。源代码及更多结果可在https://research.nvidia.com/labs/toronto-ai/xcube/ 找到。