title: VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
summary: Existing NeRF-based methods for large scene reconstruction often have
  limitations in visual quality and rendering speed. While the recent 3D Gaussian
  Splatting works well on small-scale and object-centric scenes, scaling it up to
  large scenes poses challenges due to limited video memory, long optimization
  time, and noticeable appearance variations. To address these challenges, we
  present VastGaussian, the first method for high-quality reconstruction and
  real-time rendering on large scenes based on 3D Gaussian Splatting. We propose
  a progressive partitioning strategy to divide a large scene into multiple
  cells, where the training cameras and point cloud are properly distributed with
  an airspace-aware visibility criterion. These cells are merged into a complete
  scene after parallel optimization. We also introduce decoupled appearance
  modeling into the optimization process to reduce appearance variations in the
  rendered images. Our approach outperforms existing NeRF-based methods and
  achieves state-of-the-art results on multiple large scene datasets, enabling
  fast optimization and high-fidelity real-time rendering.
paper_authors._id: 65deb77d3359945930839229
paper_authors.name: Jiaqi Lin
paper_authors._id: 65deb77d335994593083922a
paper_authors.name: Zhihao Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-29T08:40:38.180Z
paper_authors._id: 65dff771574e5aa0e9c119fa
paper_authors.avatarUrl: /avatars/286bcf0937d2f913ec8ae4f78bbbe272.svg
paper_authors.fullname: Zhihao Li
paper_authors.type: user
paper_authors.user: JoeyLee777
paper_authors._id: 65deb77d335994593083922b
paper_authors.name: Xiao Tang
paper_authors._id: 65deb77d335994593083922c
paper_authors.name: Jianzhuang Liu
paper_authors._id: 65deb77d335994593083922d
paper_authors.name: Shiyong Liu
paper_authors._id: 65deb77d335994593083922e
paper_authors.name: Jiayue Liu
paper_authors._id: 65deb77d335994593083922f
paper_authors.name: Yangdi Lu
paper_authors._id: 65deb77d3359945930839230
paper_authors.name: Xiaofei Wu
paper_authors._id: 65deb77d3359945930839231
paper_authors.name: Songcen Xu
paper_authors._id: 65deb77d3359945930839232
paper_authors.name: Youliang Yan
paper_authors._id: 65deb77d3359945930839233
paper_authors.name: Wenming Yang
paper_title: VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
paper_summary: Existing NeRF-based methods for large scene reconstruction often have
  limitations in visual quality and rendering speed. While the recent 3D Gaussian
  Splatting works well on small-scale and object-centric scenes, scaling it up to
  large scenes poses challenges due to limited video memory, long optimization
  time, and noticeable appearance variations. To address these challenges, we
  present VastGaussian, the first method for high-quality reconstruction and
  real-time rendering on large scenes based on 3D Gaussian Splatting. We propose
  a progressive partitioning strategy to divide a large scene into multiple
  cells, where the training cameras and point cloud are properly distributed with
  an airspace-aware visibility criterion. These cells are merged into a complete
  scene after parallel optimization. We also introduce decoupled appearance
  modeling into the optimization process to reduce appearance variations in the
  rendered images. Our approach outperforms existing NeRF-based methods and
  achieves state-of-the-art results on multiple large scene datasets, enabling
  fast optimization and high-fidelity real-time rendering.
paper_ai_summary: VastGaussian addresses limitations in NeRF-based methods for large scene reconstruction by partitioning scenes and decoupling appearance modeling, achieving high-quality and real-time rendering.
paper_ai_keywords: NeRF 3D Gaussian Splatting VastGaussian progressive partitioning airspace-aware visibility criterion decoupled appearance modeling