We present Stochastic Gaussian Splatting (SGS): the first framework for uncertainty estimation using Gaussian Splatting (GS). GS recently advanced the novel-view synthesis field by achieving impressive reconstruction quality at a fraction of the computational cost of Neural Radiance Fields (NeRF). However, contrary to the latter, it still lacks the ability to provide information about the confidence associated with their outputs. To address this limitation, in this paper, we introduce a Variational Inference-based approach that seamlessly integrates uncertainty prediction into the common rendering pipeline of GS. Additionally, we introduce the Area Under Sparsification Error (AUSE) as a new term in the loss function, enabling optimization of uncertainty estimation alongside image reconstruction. Experimental results on the LLFF dataset demonstrate that our method outperforms existing approaches in terms of both image rendering quality and uncertainty estimation accuracy. Overall, our framework equips practitioners with valuable insights into the reliability of synthesized views, facilitating safer decision-making in real-world applications.

我们提出了使用随机高斯扩散（SGS）的不确定性估计框架，该框架将不确定性预测与高斯扩散的常见渲染流程无缝集成，同时引入了稀疏误差下的面积（AUSE）作为损失函数的新项，实现了图像重建和不确定性估计的优化。实验结果表明，我们的方法在图像渲染质量和不确定性估计精度方面优于现有方法。总体而言，我们的框架为从事合成视图的个体提供了有关可靠性的有价值的信息，从而在现实世界的应用中促进了更安全的决策。

高斯点插值的不确定性建模