SVGS：利用空间可变色彩基元增强高斯泼溅技术

摘要

基於高斯顯式表徵的多視角重建中，高斯潑濺技術展現出令人矚目的效果。然而現有高斯基元僅具備單一的視角依賴色彩與不透明度來表徵場景外觀與幾何結構，導致其表徵形式不夠緊湊。本文提出名為SVGS（空間變異高斯潑濺）的新方法，通過在單個高斯基元中引入空間變異的色彩與不透明度來提升表徵能力。我們實現了雙線性插值、可移動核函數以及微型神經網絡作為空間變異函數。SVGS採用二維高斯面元作為基元，在保持高質量幾何重建的同時，顯著提升了新視角合成效果。該方法在實際應用中尤其有效，因為複雜紋理與相對簡單幾何結構相結合的場景在真實環境中普遍存在。定量與定性實驗結果表明，三種函數均超越基準方法，其中可移動核函數在多個數據集上實現了最優的新視角合成性能，彰顯了空間變異函數的強大潛力。項目頁面：https://ruixu.me/html/SuperGaussians/index.html

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

SVGS：利用空间可变色彩基元增强高斯泼溅技术

SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors

摘要

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