title: GaussianCube: Structuring Gaussian Splatting using Optimal Transport for
    3D Generative Modeling
summary: 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.
paper_authors._id: 660642d0b85b7b4ea5233a91
paper_authors.name: Bowen Zhang
paper_authors._id: 660642d0b85b7b4ea5233a92
paper_authors.name: Yiji Cheng
paper_authors._id: 660642d0b85b7b4ea5233a93
paper_authors.name: Jiaolong Yang
paper_authors._id: 660642d0b85b7b4ea5233a94
paper_authors.name: Chunyu Wang
paper_authors._id: 660642d0b85b7b4ea5233a95
paper_authors.name: Feng Zhao
paper_authors._id: 660642d0b85b7b4ea5233a96
paper_authors.name: Yansong Tang
paper_authors._id: 660642d0b85b7b4ea5233a97
paper_authors.name: Dong Chen
paper_authors._id: 660642d0b85b7b4ea5233a98
paper_authors.name: Baining Guo
paper_title: GaussianCube: Structuring Gaussian Splatting using Optimal Transport for
    3D Generative Modeling
paper_summary: 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.
paper_ai_summary: GaussianCube, a structured Gaussian Splatting representation, enables efficient and high-quality generative modeling using diffusion models and U-Net architecture.
paper_ai_keywords: Gaussian Splatting Neural Radiance Fields GaussianCube densification-constrained GS fitting algorithm Optimal Transport 3D U-Net diffusion generative modeling ShapeNet OmniObject3D