A Grid-Based Hierarchical Representation Method for Large-Scale Scene Based on 3DGS

Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct application in large-scale 3D scene reconstruction. To address this, this paper proposes a novel grid-based scene segmentation technique in the process of reconstruction. Sparse point clouds, acting as an indirect input for 3DGS, are first processed by Z-Score and percentile-based filter to prepare the pure scene for segmentation. Then, through grid creation, grid partitioning, and grid merging, it forms rational and widely-applicable sub-grids and sub-scenes for training. Followed by integrating Hierarchy-GS's LOD strategy, this method achieves better large-scale reconstruction effect within limited computational resources. Experiments on multiple datasets show that this method matches others in single block reconstruction and excels in complete scene reconstruction, achieving superior results in PSNR, LPIPS, SSIM, and visualization quality.