Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

• URL: http://arxiv.org/abs/2603.22786v1
• Date: Tue, 24 Mar 2026 04:21:42 GMT
• Title: Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
• Abstract summary: We introduce a lightweight, plug-and-play framework for pixel-wise, view-dependent predictive uncertainty estimation.<n>Our method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals.<n>This architecture-agnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation.
• Score: 9.936999820242244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map for autonomous agents and safety-critical applications, knowing where the representation is uncertain is as important as the rendering fidelity itself. We bridge this critical gap by introducing a lightweight, plug-and-play framework for pixel-wise, view-dependent predictive uncertainty estimation. Our post-hoc method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals. This architecture-agnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation or degrading baseline visual fidelity. Crucially, we demonstrate that providing this actionable reliability signal successfully translates 3D Gaussian splatting into a trustworthy spatial map, further improving state-of-the-art performance across three critical downstream perception tasks: active view selection, pose-agnostic scene change detection, and pose-agnostic anomaly detection.

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