为3D高斯点阵添加视角依赖的不确定性建模，提升重建质量
View-Dependent Uncertainty Estimation of 3D Gaussian Splatting
- 用球谐函数建模每个高斯的视角依赖不确定性
- 在真实场景数据上比集成方法快10倍以上，精度不降
- 适合需要可靠几何与颜色估计的下游任务
3D高斯点阵（3DGS）因其出色的视觉保真度，在三维场景重建中日益流行。然而，3DGS场景的不确定性估计仍缺乏研究，这对资产提取和场景补全等下游任务至关重要。由于高斯点的颜色具有视角依赖性，从某一角度可能确定，另一角度则不确定。为此，我们提出将不确定性作为额外的视角依赖型每高斯特征进行建模，采用球谐函数表示。该方法简单有效，易于解释，并可无缝集成至传统3DGS流程中。实验表明，该方法显著快于集成方法，同时保持高精度。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has become increasingly popular in 3D scene reconstruction for its high visual accuracy. However, uncertainty estimation of 3DGS scenes remains underexplored and is crucial to downstream tasks such as asset extraction and scene completion. Since the appearance of 3D gaussians is view-dependent, the color of a gaussian can thus be certain from an angle and uncertain from another. We thus propose to model uncertainty in 3DGS as an additional view-dependent per-gaussian feature that can be modeled with spherical harmonics. This simple yet effective modeling is easily interpretable and can be integrated into the traditional 3DGS pipeline. It is also significantly faster than ensemble methods while maintaining high accuracy, as demonstrated in our experiments.
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