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.

翻译：现有基于NeRF的大场景重建方法在视觉质量和渲染速度上常存在局限。虽然近期提出的三维高斯泼溅技术在小规模及物体中心场景中表现优异，但将其扩展至大场景时面临显存不足、优化耗时长及外观差异显著等挑战。针对这些问题，我们提出VastGaussian——首个基于三维高斯泼溅实现大场景高质量重建与实时渲染的方法。该方法提出渐进式分区策略：通过空域感知的可见性准则，将大场景划分为多个训练相机与点云分布合理的单元，经并行优化后合并为完整场景。此外，我们在优化过程中引入解耦外观建模以降低渲染图像中的表观差异。本方法在多个大场景数据集上超越现有NeRF方法并取得最优结果，实现快速优化与高保真实时渲染。