Gaussian Splattings demonstrate 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 SuperGaussians 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 even tiny neural networks as spatially varying functions. 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.
高斯点云表示（Gaussian Splattings） 在基于高斯显式表示的多视图重建中表现出色。然而，目前的高斯原语仅通过单一的视图依赖颜色和不透明度表示场景的外观和几何结构，导致表示能力不足且不紧凑。 本文提出了一种新方法 SuperGaussians，通过在单个高斯原语中引入空间变化颜色和不透明度，提升其表示能力。我们实现了三种空间变化函数：双线性插值、可移动核和微型神经网络。实验表明，这些方法均优于传统基线，尤其是可移动核在多个数据集的新视图合成任务中表现最优，展现了卓越性能。 实验验证了空间变化函数的潜力，不仅提升了表示效率，还增强了新视图合成的效果，为高斯点云的多视图重建提供了更紧凑高效的解决方案。