SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors
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| Authors | Rui Xu et al. |
| Year | 2026 |
| HF Upvotes | 8 |
| arXiv | 2411.18966 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Gaussian Splatting demonstrates impressive results in multi-view reconstruction based on Gaussian explicit representations. However, the current Gaussian primitives only have a single view-dependent color and an opacity to represent the appearance and geometry of the scene, resulting in a non-compact representation. In this paper, we introduce a new method called SVGS (Spatially Varying Gaussian Splatting) that utilizes spatially varying colors and opacity in a single Gaussian primitive to improve its representation ability. We have implemented bilinear interpolation, movable kernels, and tiny neural networks as spatially varying functions. SVGS employs 2D Gaussian surfels as primitives, which significantly enhances novel-view synthesis while maintaining high-quality geometric reconstruction. This approach is particularly effective in practical applications, as scenes combining complex textures with relatively simple geometry occur frequently in real-world environments. Quantitative and qualitative experimental results demonstrate that all three functions outperform the baseline, with the best movable kernels achieving superior novel view synthesis performance on multiple datasets, highlighting the strong potential of spatially varying functions. Project page: https://ruixu.me/html/SuperGaussians/index.html
Engineering Breakdown
Plain English
This paper improves Gaussian Splatting (a 3D reconstruction technique) by adding spatially varying colors and opacity within individual Gaussian primitives instead of using single fixed colors per primitive. The authors tested three implementations—bilinear interpolation, movable kernels, and tiny neural networks—and show this approach achieves better novel-view synthesis quality while keeping geometric reconstruction quality high and reducing memory footprint.
Key Engineering Insight
Instead of storing one view-dependent color per Gaussian primitive, you can store a spatial color field within each primitive using lightweight functions (interpolation or small MLPs). This dramatically increases representation capacity without proportional memory cost, which is the core efficiency gain for production deployment.
Why It Matters for Engineers
3D reconstruction systems hit a hard tradeoff: add more primitives for quality but tank memory/latency, or keep primitives sparse and lose detail. Spatially varying colors inside primitives sidestep this—you get better quality at similar compute cost. This directly impacts real-time rendering pipelines and mobile deployment where both memory and inference speed are constraints.
Research Context
Gaussian Splatting exploded in popularity because it's faster than NeRF-based approaches for novel-view synthesis, but earlier work treated each Gaussian as a single-colored blob, forcing the method to use many primitives for detail. SVGS advances the primitive itself to be more expressive, following the pattern of moving explicit representations toward implicit-like capacity. This enables either faster rendering at same quality or higher quality at same speed—both valuable for deployment.
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