  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.
