BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via
  Adaptive Block-Based Gaussian Splatting
Authors: name: Yongchang Wu, name: Zipeng Qi, name: Zhenwei Shi, name: Zhengxia Zou

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