title: SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh
    Reconstruction and High-Quality Mesh Rendering
summary: We propose a method to allow precise and extremely fast mesh extraction from
  3D Gaussian Splatting. Gaussian Splatting has recently become very popular as
  it yields realistic rendering while being significantly faster to train than
  NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D
  gaussians as these gaussians tend to be unorganized after optimization and no
  method has been proposed so far. Our first key contribution is a regularization
  term that encourages the gaussians to align well with the surface of the scene.
  We then introduce a method that exploits this alignment to extract a mesh from
  the Gaussians using Poisson reconstruction, which is fast, scalable, and
  preserves details, in contrast to the Marching Cubes algorithm usually applied
  to extract meshes from Neural SDFs. Finally, we introduce an optional
  refinement strategy that binds gaussians to the surface of the mesh, and
  jointly optimizes these Gaussians and the mesh through Gaussian splatting
  rendering. This enables easy editing, sculpting, rigging, animating,
  compositing and relighting of the Gaussians using traditional softwares by
  manipulating the mesh instead of the gaussians themselves. Retrieving such an
  editable mesh for realistic rendering is done within minutes with our method,
  compared to hours with the state-of-the-art methods on neural SDFs, while
  providing a better rendering quality.
paper_authors._id: 655d91a45119b63e7aa66d99
paper_authors.name: Antoine Guédon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T13:42:11.823Z
paper_authors._id: 6489dd894bbed6144c870274
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6489dd894bbed6144c870274/c_J9b4DHwXeE5VtTeQ2ue.jpeg
paper_authors.fullname: Antoine Guédon
paper_authors.type: user
paper_authors.user: AntoineGuedon
paper_authors._id: 655d91a45119b63e7aa66d9a
paper_authors.name: Vincent Lepetit
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T13:42:18.427Z
paper_authors._id: 6353c6045eac2d2efa74a06f
paper_authors.avatarUrl: /avatars/b2102600513a08b3f96df4af53dcee98.svg
paper_authors.fullname: Vincent Lepetit
paper_authors.type: user
paper_authors.user: miettle
paper_title: SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh
    Reconstruction and High-Quality Mesh Rendering
paper_summary: We propose a method to allow precise and extremely fast mesh extraction from
  3D Gaussian Splatting. Gaussian Splatting has recently become very popular as
  it yields realistic rendering while being significantly faster to train than
  NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D
  gaussians as these gaussians tend to be unorganized after optimization and no
  method has been proposed so far. Our first key contribution is a regularization
  term that encourages the gaussians to align well with the surface of the scene.
  We then introduce a method that exploits this alignment to extract a mesh from
  the Gaussians using Poisson reconstruction, which is fast, scalable, and
  preserves details, in contrast to the Marching Cubes algorithm usually applied
  to extract meshes from Neural SDFs. Finally, we introduce an optional
  refinement strategy that binds gaussians to the surface of the mesh, and
  jointly optimizes these Gaussians and the mesh through Gaussian splatting
  rendering. This enables easy editing, sculpting, rigging, animating,
  compositing and relighting of the Gaussians using traditional softwares by
  manipulating the mesh instead of the gaussians themselves. Retrieving such an
  editable mesh for realistic rendering is done within minutes with our method,
  compared to hours with the state-of-the-art methods on neural SDFs, while
  providing a better rendering quality.
paper_ai_keywords: Gaussian Splatting NeRFs Poisson reconstruction Marching Cubes algorithm Neural SDFs Gaussian splatting rendering