Optimizing 3D Gaussian Splatting via Point Cloud Upsampling
3D Gaussian Splatting (3DGS) is a technique for creating and rendering 3D scenes, however its performance depends heavily on the quality of initial seed points. To improve 3DGS initialization, this study presents and evaluates several point cloud upsampling approaches: linear interpolation, triangular interpolation, spline-based surface reconstruction, moving least squares surface fitting, and Voronoi-based point generation. Additionally, this research introduces a depth-guided point lifting method that leverages depth maps to maintain geometric consistency with Structure-from-Motion (SfM) reconstructions. Through extensive experiments on the Mip-NeRF360 and Replica datasets, the proposed...
| Authors | Adrian Ramlal, Yan Song Hu, John S. Zelek |
|---|---|
| Primary category | cs.CV |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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