title: Does Gaussian Splatting need SFM Initialization?
summary: 3D Gaussian Splatting has recently been embraced as a versatile and effective
  method for scene reconstruction and novel view synthesis, owing to its
  high-quality results and compatibility with hardware rasterization. Despite its
  advantages, Gaussian Splatting's reliance on high-quality point cloud
  initialization by Structure-from-Motion (SFM) algorithms is a significant
  limitation to be overcome. To this end, we investigate various initialization
  strategies for Gaussian Splatting and delve into how volumetric reconstructions
  from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on
  SFM data. Our findings demonstrate that random initialization can perform much
  better if carefully designed and that by employing a combination of improved
  initialization strategies and structure distillation from low-cost NeRF models,
  it is possible to achieve equivalent results, or at times even superior, to
  those obtained from SFM initialization.
paper_authors._id: 6625ef4fc322e4c1482af044
paper_authors.name: Yalda Foroutan
paper_authors._id: 6625ef4fc322e4c1482af045
paper_authors.name: Daniel Rebain
paper_authors._id: 6625ef4fc322e4c1482af046
paper_authors.name: Kwang Moo Yi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-22T14:04:23.987Z
paper_authors._id: 64a591af9b8ae9ccccfdd3e7
paper_authors.avatarUrl: /avatars/c6cd43d814956261639fe594bc4aa181.svg
paper_authors.fullname: Kwang Moo Yi
paper_authors.type: user
paper_authors.user: kmyi
paper_authors._id: 6625ef4fc322e4c1482af047
paper_authors.name: Andrea Tagliasacchi
paper_title: Does Gaussian Splatting need SFM Initialization?
paper_summary: 3D Gaussian Splatting has recently been embraced as a versatile and effective
  method for scene reconstruction and novel view synthesis, owing to its
  high-quality results and compatibility with hardware rasterization. Despite its
  advantages, Gaussian Splatting's reliance on high-quality point cloud
  initialization by Structure-from-Motion (SFM) algorithms is a significant
  limitation to be overcome. To this end, we investigate various initialization
  strategies for Gaussian Splatting and delve into how volumetric reconstructions
  from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on
  SFM data. Our findings demonstrate that random initialization can perform much
  better if carefully designed and that by employing a combination of improved
  initialization strategies and structure distillation from low-cost NeRF models,
  it is possible to achieve equivalent results, or at times even superior, to
  those obtained from SFM initialization.
paper_ai_keywords: Gaussian Splatting scene reconstruction, novel view synthesis, linear reconstruction, high-quality point cloud, Structure-from-Motion (SFM) algorithms, volumetric reconstructions, Neural Radiance Fields (NeRF), random initialization, end-to-end, NeRF models