用可变颜色的高斯点提升三维重建质量，让新视角更逼真。
SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors
- 每个高斯点支持空间变化的颜色和透明度，增强表达能力。
- 移动核函数在多个数据集上显著提升新视角生成效果。
- 适合纹理复杂但几何简单的现实场景，实用性强。
高斯点绘（Gaussian Splatting）基于显式高斯表示，在多视角重建中表现优异。然而，现有高斯原语仅具单一视图依赖颜色与不透明度，导致表示不紧凑。本文提出新方法SVGS（空间可变高斯点绘），在单个高斯原语中引入空间变化的颜色与不透明度，以提升表征能力。我们实现了双线性插值、可移动核函数及微型神经网络作为空间变化函数。SVGS采用2D高斯面元作为原语，显著提升新视角合成质量，同时保持高质量几何重建。该方法在实际应用中尤为有效，因真实环境中常出现纹理复杂而几何简单的情况。定量与定性实验表明，三种函数均优于基线，其中最佳移动核函数在多个数据集上实现更优的新视角合成性能，凸显空间可变函数的强大潜力。
原文摘要 · Abstract (English)
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
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