VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue, Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, Wenming Yang

TL;DR
VastGaussian introduces a scalable 3D Gaussian Splatting method for large scene reconstruction that achieves high-quality, real-time rendering by using scene partitioning and decoupled appearance modeling.
Contribution
It presents the first scalable approach for large scene reconstruction with 3D Gaussian Splatting, enabling fast optimization and high-fidelity rendering.
Findings
Outperforms existing NeRF-based methods on large scenes.
Achieves state-of-the-art results in quality and speed.
Enables real-time rendering of large-scale scenes.
Abstract
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Image Processing and 3D Reconstruction
