View-Structured Conformal Prediction for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) renders novel views in real time, but an uncertainty heatmap only ranks pixels. It does not say how wide an interval must be for a view to meet a coverage target. We treat novel-view synthesis as structured regression and ask that, with probability at least $1-\alpha$, RGB prediction boxes cover at least a $1-\beta$ fraction of pixels in a new view. We propose View-Structured Conformal Prediction (VSCP). It splits the pre-calibration scale into a spatial shape from the renderer and a transferable view-difficulty factor, which predicts the smallest view-wise multiplier that shape needs. A held-out quantile over views (View-CP) then gives finite-sample validity even when transferring to new scenes. The same factorization makes the analysis exact. A conformity score is the ratio of oracle to predicted view difficulty, and excess width separates into a test-side and a calibration-side term. Across 13 real scenes, pixel-pooled calibration reaches 89.9% marginal pixel coverage but only 61.4% view-event coverage at a 90% target, while View-CP reaches 91.7–92.0%. At matched coverage VSCP cuts width by 22.1% against a constant scale, and matches a ten-model ensemble's 21.0% reduction using only one model per scene and four rather than ten rasterization passes per query. VSCP also improves on the closest single-model baseline, the 3DGS-U field, by 4.7 points ($p=0.0225$). The view predictor transfers from bounded source families to all nine unbounded Mip-NeRF360 scenes. There VSCP saves 23.5% width on average and beats the constant scale on all nine scenes. It also keeps a 20.6% saving under a different densification backbone and runs at 216–280 FPS on an RTX 4090.