title: BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via
    Adaptive Block-Based Gaussian Splatting
summary: The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated
  remarkable potential in novel view synthesis tasks. The divide-and-conquer
  paradigm has enabled large-scale scene reconstruction, but significant
  challenges remain in scene partitioning, optimization, and merging processes.
  This paper introduces BlockGaussian, a novel framework incorporating a
  content-aware scene partition strategy and visibility-aware block optimization
  to achieve efficient and high-quality large-scale scene reconstruction.
  Specifically, our approach considers the content-complexity variation across
  different regions and balances computational load during scene partitioning,
  enabling efficient scene reconstruction. To tackle the supervision mismatch
  issue during independent block optimization, we introduce auxiliary points
  during individual block optimization to align the ground-truth supervision,
  which enhances the reconstruction quality. Furthermore, we propose a
  pseudo-view geometry constraint that effectively mitigates rendering
  degradation caused by airspace floaters during block merging. Extensive
  experiments on large-scale scenes demonstrate that our approach achieves
  state-of-the-art performance in both reconstruction efficiency and rendering
  quality, with a 5x speedup in optimization and an average PSNR improvement of
  1.21 dB on multiple benchmarks. Notably, BlockGaussian significantly reduces
  computational requirements, enabling large-scale scene reconstruction on a
  single 24GB VRAM device. The project page is available at
  https://github.com/SunshineWYC/BlockGaussian
paper_authors._id: 67fff3a82d8bee24b1c1d7d7
paper_authors.name: Yongchang Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-17T08:05:03.693Z
paper_authors._id: 644a8050236745531574706e
paper_authors.avatarUrl: /avatars/298f1ea4bcf139e66b7ab74645799961.svg
paper_authors.fullname: Yongchang WU
paper_authors.type: user
paper_authors.user: SunshineWu
paper_authors._id: 67fff3a82d8bee24b1c1d7d8
paper_authors.name: Zipeng Qi
paper_authors._id: 67fff3a82d8bee24b1c1d7d9
paper_authors.name: Zhenwei Shi
paper_authors._id: 67fff3a82d8bee24b1c1d7da
paper_authors.name: Zhengxia Zou
paper_title: BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via
    Adaptive Block-Based Gaussian Splatting
paper_summary: The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated
  remarkable potential in novel view synthesis tasks. The divide-and-conquer
  paradigm has enabled large-scale scene reconstruction, but significant
  challenges remain in scene partitioning, optimization, and merging processes.
  This paper introduces BlockGaussian, a novel framework incorporating a
  content-aware scene partition strategy and visibility-aware block optimization
  to achieve efficient and high-quality large-scale scene reconstruction.
  Specifically, our approach considers the content-complexity variation across
  different regions and balances computational load during scene partitioning,
  enabling efficient scene reconstruction. To tackle the supervision mismatch
  issue during independent block optimization, we introduce auxiliary points
  during individual block optimization to align the ground-truth supervision,
  which enhances the reconstruction quality. Furthermore, we propose a
  pseudo-view geometry constraint that effectively mitigates rendering
  degradation caused by airspace floaters during block merging. Extensive
  experiments on large-scale scenes demonstrate that our approach achieves
  state-of-the-art performance in both reconstruction efficiency and rendering
  quality, with a 5x speedup in optimization and an average PSNR improvement of
  1.21 dB on multiple benchmarks. Notably, BlockGaussian significantly reduces
  computational requirements, enabling large-scale scene reconstruction on a
  single 24GB VRAM device. The project page is available at
  https://github.com/SunshineWYC/BlockGaussian
paper_ai_summary: BlockGaussian achieves efficient and high-quality large-scale 3D scene reconstruction by incorporating content-aware partitioning, visibility-aware optimization, and pseudo-view geometry constraints during merging.
paper_ai_keywords: Gaussian Splatting 3DGS divide-and-conquer scene partitioning optimization merging content-aware visibility-aware auxiliary points ground-truth supervision pseudo-view geometry constraint 3D scene reconstruction rendering quality PSNR VRAM