BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via
  Adaptive Block-Based Gaussian Splatting
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
67fff3a82d8bee24b1c1d7d7
Yongchang Wu
claimed_verified
2025-04-17T08:05:03.693Z
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/avatars/298f1ea4bcf139e66b7ab74645799961.svg
Yongchang WU
user
SunshineWu
67fff3a82d8bee24b1c1d7d8
Zipeng Qi
67fff3a82d8bee24b1c1d7d9
Zhenwei Shi
67fff3a82d8bee24b1c1d7da
Zhengxia Zou
BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via
  Adaptive Block-Based Gaussian Splatting
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
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.
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