# A New Split Algorithm for 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2403.09143 · HTML: https://mlsanity.com/paper/2403.09143/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [Recent Advances in 3D Gaussian Splatting](https://mlsanity.com/paper/2403.11134/) (2024)
- [Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos](https://mlsanity.com/paper/2406.18717/) (2024)
- [3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2407.17418/) (2024)
- [VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction](https://mlsanity.com/paper/2506.05563/) (2025)
- [Variation-aware Flexible 3D Gaussian Editing](https://mlsanity.com/paper/2602.11638/) (2026)
- [Gaussian Set Surface Reconstruction through Per-Gaussian Optimization](https://mlsanity.com/paper/2507.18923/) (2025)

## Abstract (arXiv)

3D Gaussian splatting models, as a novel explicit 3D representation, have been applied in many domains recently, such as explicit geometric editing and geometry generation. Progress has been rapid. However, due to their mixed scales and cluttered shapes, 3D Gaussian splatting models can produce a blurred or needle-like effect near the surface. At the same time, 3D Gaussian splatting models tend to flatten large untextured regions, yielding a very sparse point cloud. These problems are caused by the non-uniform nature of 3D Gaussian splatting models, so in this paper, we propose a new 3D Gaussian splitting algorithm, which can produce a more uniform and surface-bounded 3D Gaussian splatting model. Our algorithm splits an N-dimensional Gaussian into two N-dimensional Gaussians. It ensures consistency of mathematical characteristics and similarity of appearance, allowing resulting 3D Gaussian splatting models to be more uniform and a better fit to the underlying surface, and thus more suitable for explicit editing, point cloud extraction and other tasks. Meanwhile, our 3D Gaussian splitting approach has a very simple closed-form solution, making it readily applicable to any 3D Gaussian model.