Główne pojęcia
Efficiently represent scenes with constrained Gaussians through densification and simplification.
Streszczenie
The study explores representing scenes with a limited number of Gaussians, focusing on spatial distribution optimization. Techniques like blur splitting and depth reinitialization enhance rendering quality and efficiency. Mini-Splatting method integrates seamlessly for future research in Gaussian-Splatting-based works.
- Introduction:
- 3D Gaussian Splatting (3DGS) showcases potential in various applications.
- Challenges in efficiently representing scenes with millions of Gaussians.
- Related Work:
- Comparison between 3DGS and traditional rendering techniques.
- Focus on preserving feature points or geometric structures in 3D data simplification algorithms.
- Analyzing 3DGS from Point Clouds Perspective:
- Preliminaries of Gaussian Splatting and adaptive density control of Gaussians.
- Methodology:
- Densification strategies like blur split and depth reinitialization for dense Gaussian distribution.
- Applications:
- Variants of Mini-Splatting tailored for different priorities - resource-efficient training, quality-prioritized rendering, and storage compression.
- Experiments:
- Evaluation on real-world datasets showcasing superior performance compared to baseline methods.
- Conclusion:
- Mini-Splatting offers an effective solution for scene representation with a limited number of Gaussians, balancing rendering quality, resource consumption, and storage compression.
Statystyki
Train: 33min, PSNR: 25.5dB
Train: 35min, PSNR: 25.2dB
Train: 17min, PSNR: 25.2dB