title: FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting
summary: Novel view synthesis from limited observations remains an important and
  persistent task. However, high efficiency in existing NeRF-based few-shot view
  synthesis is often compromised to obtain an accurate 3D representation. To
  address this challenge, we propose a few-shot view synthesis framework based on
  3D Gaussian Splatting that enables real-time and photo-realistic view synthesis
  with as few as three training views. The proposed method, dubbed FSGS, handles
  the extremely sparse initialized SfM points with a thoughtfully designed
  Gaussian Unpooling process. Our method iteratively distributes new Gaussians
  around the most representative locations, subsequently infilling local details
  in vacant areas. We also integrate a large-scale pre-trained monocular depth
  estimator within the Gaussians optimization process, leveraging online
  augmented views to guide the geometric optimization towards an optimal
  solution. Starting from sparse points observed from limited input viewpoints,
  our FSGS can accurately grow into unseen regions, comprehensively covering the
  scene and boosting the rendering quality of novel views. Overall, FSGS achieves
  state-of-the-art performance in both accuracy and rendering efficiency across
  diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website:
  https://zehaozhu.github.io/FSGS/.
paper_authors._id: 656db8e1ca3e0756db3362ad
paper_authors.name: Zehao Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T14:37:22.572Z
paper_authors._id: 63611b5b6bd72c97d0fe5dc8
paper_authors.avatarUrl: /avatars/8905d4c0a930c7ba6e057c8b01f22a2e.svg
paper_authors.fullname: Zehao Zhu
paper_authors.type: user
paper_authors.user: zzhzzh
paper_authors._id: 656db8e1ca3e0756db3362ae
paper_authors.name: Zhiwen Fan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T14:37:34.011Z
paper_authors._id: 6526386e1c6a09292d8d0a22
paper_authors.avatarUrl: /avatars/471de830de2d775d35368678c1579f87.svg
paper_authors.fullname: fan
paper_authors.type: user
paper_authors.user: Fanzhiwen
paper_authors._id: 656db8e1ca3e0756db3362af
paper_authors.name: Yifan Jiang
paper_authors._id: 656db8e1ca3e0756db3362b0
paper_authors.name: Zhangyang Wang
paper_title: FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting
paper_summary: Novel view synthesis from limited observations remains an important and
  persistent task. However, high efficiency in existing NeRF-based few-shot view
  synthesis is often compromised to obtain an accurate 3D representation. To
  address this challenge, we propose a few-shot view synthesis framework based on
  3D Gaussian Splatting that enables real-time and photo-realistic view synthesis
  with as few as three training views. The proposed method, dubbed FSGS, handles
  the extremely sparse initialized SfM points with a thoughtfully designed
  Gaussian Unpooling process. Our method iteratively distributes new Gaussians
  around the most representative locations, subsequently infilling local details
  in vacant areas. We also integrate a large-scale pre-trained monocular depth
  estimator within the Gaussians optimization process, leveraging online
  augmented views to guide the geometric optimization towards an optimal
  solution. Starting from sparse points observed from limited input viewpoints,
  our FSGS can accurately grow into unseen regions, comprehensively covering the
  scene and boosting the rendering quality of novel views. Overall, FSGS achieves
  state-of-the-art performance in both accuracy and rendering efficiency across
  diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website:
  https://zehaozhu.github.io/FSGS/.
paper_ai_summary: FSGS, a few-shot view synthesis framework using 3D Gaussian Splatting, achieves real-time photo-realistic rendering with three training views and state-of-the-art accuracy and efficiency.
paper_ai_keywords: NeRF few-shot view synthesis 3D Gaussian Splatting Gaussian Unpooling monocular depth estimator geometric optimization rendering quality LLFF Mip-NeRF360 Blender