We present $\mathcal{X}^3$ (pronounced XCube), 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. More results and details can be found at https://research.nvidia.com/labs/toronto-ai/xcube/.

我们提出了一种名为XCube的新颖生成模型，用于高分辨率稀疏3D体素网格和任意属性。我们的模型可以在前向传递的方式下生成数百万个体素，具有高达1024^3的最佳有效分辨率，而无需耗时的测试时间优化。除了生成高分辨率的物体外，我们还展示了XCube在100m×100m尺度的大型室外场景上的有效性，体素大小可以达到10cm。我们观察到相比过去的方法，有明显的定性和定量改进。除了无条件生成，我们还展示了我们的模型可以用于解决各种任务，如用户引导编辑，从单个扫描完成场景，以及文本到3D。更多结果和详情可以在此URL找到。

XCube（X³）：使用稀疏体素层次化进行大规模3D生成建模