  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.
