Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
This enables reliable spatial mapping for autonomous agents and safety-critical applications by addressing uncertainty in 3D representations.
The paper tackles the problem of uncertainty estimation in 3D Gaussian Splatting for novel view synthesis, introducing a lightweight framework that provides pixel-wise predictive uncertainty without degrading visual fidelity, and demonstrates improved state-of-the-art performance on three downstream perception tasks.
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.