Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Visibility

• URL: http://arxiv.org/abs/2508.02443v1
• Date: Mon, 04 Aug 2025 14:02:20 GMT
• Title: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Visibility
• Authors: Thomas Gottwald, Edgar Heinert, Matthias Rottmann,
• Abstract summary: We present a novel method for uncertainty estimation (UE) in Gaussian Splatting.<n>Our method establishes primitive representations of error and visibility of trainings views, which carries meaningful uncertainty information.<n>Our UEs show high correlations to true errors, outperforming state-of-the-art methods, especially on foreground objects.
• Score: 4.69726714177332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present a novel method for uncertainty estimation (UE) in Gaussian Splatting. UE is crucial for using Gaussian Splatting in critical applications such as robotics and medicine. Previous methods typically estimate the variance of Gaussian primitives and use the rendering process to obtain pixel-wise uncertainties. Our method establishes primitive representations of error and visibility of trainings views, which carries meaningful uncertainty information. This representation is obtained by projection of training error and visibility onto the primitives. Uncertainties of novel views are obtained by rendering the primitive representations of uncertainty for those novel views, yielding uncertainty feature maps. To aggregate these uncertainty feature maps of novel views, we perform a pixel-wise regression on holdout data. In our experiments, we analyze the different components of our method, investigating various combinations of uncertainty feature maps and regression models. Furthermore, we considered the effect of separating splatting into foreground and background. Our UEs show high correlations to true errors, outperforming state-of-the-art methods, especially on foreground objects. The trained regression models show generalization capabilities to new scenes, allowing uncertainty estimation without the need for holdout data.

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