3D Gaussian Splatting (GS) have achieved considerable improvement over Neural Radiance Fields in terms of 3D fitting fidelity and rendering speed. However, this unstructured representation with scattered Gaussians poses a significant challenge for generative modeling. To address the problem, we introduce GaussianCube, a structured GS representation that is both powerful and efficient for generative modeling. We achieve this by first proposing a modified densification-constrained GS fitting algorithm which can yield high-quality fitting results using a fixed number of free Gaussians, and then re-arranging the Gaussians into a predefined voxel grid via Optimal Transport. The structured grid representation allows us to use standard 3D U-Net as our backbone in diffusion generative modeling without elaborate designs. Extensive experiments conducted on ShapeNet and OmniObject3D show that our model achieves state-of-the-art generation results both qualitatively and quantitatively, underscoring the potential of GaussianCube as a powerful and versatile 3D representation.
3D高斯喷溅（GS）在3D拟合保真度和渲染速度方面相较于神经辐射场取得了显著改进。然而，这种散布的高斯的无结构表示对于生成模型构成了重大挑战。为了解决这个问题，我们介绍了GaussianCube，一种结构化的GS表示，对于生成模型既强大又高效。我们首先提出了一种修改后的密度约束GS拟合算法，该算法使用固定数量的自由高斯可以产生高质量的拟合结果，然后通过最优传输将高斯重新排列到预定义的体素网格中。结构化网格表示允许我们在扩散生成模型中使用标准的3D U-Net作为我们的骨干网络，无需复杂设计。在ShapeNet和OmniObject3D上进行的广泛实验表明，我们的模型在质量和数量上都实现了最先进的生成结果，突显了GaussianCube作为一种强大且多用途的3D表示的潜力。