The process of 3D scene reconstruction can be affected by numerous uncertainty sources in real-world scenes. While Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (GS) achieve high-fidelity rendering, they lack built-in mechanisms to directly address or quantify uncertainties arising from the presence of noise, occlusions, confounding outliers, and imprecise camera pose inputs. In this paper, we introduce a taxonomy that categorizes different sources of uncertainty inherent in these methods. Moreover, we extend NeRF- and GS-based methods with uncertainty estimation techniques, including learning uncertainty outputs and ensembles, and perform an empirical study to assess their ability to capture the sensitivity of the reconstruction. Our study highlights the need for addressing various uncertainty aspects when designing NeRF/GS-based methods for uncertainty-aware 3D reconstruction.

本研究探讨了在真实场景中进行3D场景重建时的多种不确定性来源，填补了现有NeRF和3D高斯喷射技术在处理噪声、遮挡及模型精度缺失方面的空白。本文提出了一种分类法来描述这些不确定性，并扩展现有方法以纳入不确定性估计技术，显示了在进行不确定性感知的3D重建时考虑多种不确定性因素的重要性。

3D场景重建中的不确定性来源