Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

Recent advances in 3D Gaussian Splatting have enabled im-pressive photorealistic novel view synthesis. However, to transition froma pure rendering engine to a reliable spatial map for autonomous agentsand safety-critical applications, knowing where the representation is un-certain is as important as the rendering fidelity itself. We bridge thiscritical gap by introducing a lightweight, plug-and-play framework forpixel-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 withoutmodifying the underlying scene representation or degrading baseline vi-sual fidelity. Crucially, we demonstrate that providing this actionablereliability signal successfully translates 3D Gaussian splatting into atrustworthy spatial map, further improving state-of-the-art performanceacross three critical downstream perception tasks: active view selection,pose-agnostic scene change detection, and pose-agnostic anomaly detec-tion. Code is available at github.io/3DGS-Uncertainty.