3DGS-Uncertainty: Predictive Photometric Uncertainty for Gaussian Splatting
rsasaki0109 · x · 2026-08-20
This research introduces 3DGS-Uncertainty, a framework for predictive photometric uncertainty estimation in 3D Gaussian Splatting (3DGS) for novel view synthesis.
- Context: While 3DGS achieves impressive photorealistic rendering, reliable spatial mapping for autonomous agents requires knowing where the representation is uncertain.
- Method: A lightweight, plug-and-play framework is proposed for pixel-wise, view-dependent uncertainty estimation. It formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals as a post-hoc method.
- Advantage: This architecture-agnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation or degrading baseline rendering fidelity.
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