The paper "Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians" by Guangchi Fang and Bing Wang addresses the challenge of efficiently representing scenes using a limited number of Gaussians. The authors identify that the traditional 3D Gaussian Splatting (3DGS) method, while effective for rendering and reconstruction, suffers from inefficient spatial distribution of Gaussians, leading to issues like overlapping and under-reconstruction. To improve this, they propose a novel approach called Mini-Splatting, which includes two main components: densification and simplification.
**Densification** involves two key steps:
1. **Blur Split**: This step addresses Gaussian blur artifacts by splitting Gaussians in areas with large blurry regions.
2. **Depth Reinitialization**: This step uses dense depth information to reinitialize the Gaussian representation, improving the uniformity and density of Gaussians.
**Simplification** includes:
1. **Intersection Preserving**: This technique retains Gaussians that intersect with rays, avoiding the removal of important Gaussians.
2. **Importance-weighted Sampling**: This method uses the geometric structure of the scene to sample Gaussians, maintaining high-quality rendering while reducing the number of Gaussians.
The authors evaluate their method on multiple datasets and benchmarks, demonstrating significant improvements in rendering quality, resource consumption, and storage compression compared to existing methods. Their results show that Mini-Splatting can achieve superior performance with fewer Gaussians, making it a promising approach for efficient scene representation in 3D rendering and reconstruction tasks.The paper "Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians" by Guangchi Fang and Bing Wang addresses the challenge of efficiently representing scenes using a limited number of Gaussians. The authors identify that the traditional 3D Gaussian Splatting (3DGS) method, while effective for rendering and reconstruction, suffers from inefficient spatial distribution of Gaussians, leading to issues like overlapping and under-reconstruction. To improve this, they propose a novel approach called Mini-Splatting, which includes two main components: densification and simplification.
**Densification** involves two key steps:
1. **Blur Split**: This step addresses Gaussian blur artifacts by splitting Gaussians in areas with large blurry regions.
2. **Depth Reinitialization**: This step uses dense depth information to reinitialize the Gaussian representation, improving the uniformity and density of Gaussians.
**Simplification** includes:
1. **Intersection Preserving**: This technique retains Gaussians that intersect with rays, avoiding the removal of important Gaussians.
2. **Importance-weighted Sampling**: This method uses the geometric structure of the scene to sample Gaussians, maintaining high-quality rendering while reducing the number of Gaussians.
The authors evaluate their method on multiple datasets and benchmarks, demonstrating significant improvements in rendering quality, resource consumption, and storage compression compared to existing methods. Their results show that Mini-Splatting can achieve superior performance with fewer Gaussians, making it a promising approach for efficient scene representation in 3D rendering and reconstruction tasks.