View-Structured Conformal Prediction for 3D Gaussian Splatting
VSCP方法解决了3DGS的不确定性量化问题，在保持高覆盖率的同时显著减少计算量，提升渲染效率。
3D Gaussian Splatting(3DGS)实时渲染新视角，但无法保证渲染视图的预测覆盖率。研究人员提出View-Structured Conformal Prediction(VSCP)方法，将预校准尺度分解为渲染器的空间形状和可转移的视图难度因子。在13个真实场景测试中，VSCP在90%目标覆盖率下达到91.7-92.0%的视图事件覆盖率，比常规模度减少22.1%的宽度，仅需单模型和4次光栅化查询即可达到10模型集成效果。
3D Gaussian Splatting (3DGS) renders novel views in real time, but an uncertainty heatmap does not certify that a rendered view meets a certain prediction coverage. We treat novel-view synthesis as structured regression and ask that, with probability at least $1-α$, RGB prediction boxes cover at least a $1-β$ 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-NeRF~360 scenes. There the full scale beats the constant scale with 20.7\% width saving on all nine scenes. It also keeps an 18.3\% saving under a different densification backbone and runs at 216--280 FPS on an RTX~4090.