合成立体数据中存在未被发现的视角方差相关性，可能影响模型训练效果。
Cross-View Variance Correlation in Path-Traced Stereo:A Hidden Shortcut in Synthetic Training Data
- 发现双视角渲染图像的像素方差场在真实视差对齐后高度相关
- 相关系数达0.754±0.016，且在16倍采样率下基本不变
- 该信号仅存在于蒙特卡洛渲染数据中，或为模拟到现实的隐藏捷径
路径追踪生成的合成立体数据构成了现代视差估计训练流程的重要基础。本文报告了一种此前未被识别的特性：尽管两台相机的蒙特卡洛（MC）噪声流统计独立，但其底层的方差场——即由渲染积分函数决定的逐像素确定性函数——在经真实视差扭曲对齐后表现出高度相关性。在使用Mitsuba 3渲染的20个场景中，扭曲后的皮尔逊相关系数为ρ=0.754±0.016（每像素采样数SPP=512），在代表性场景中，该相关系数在16倍采样率范围内保持稳定（ρ=0.778±0.001）。该效应在朗伯区域最强（ρ≈0.78），在玻璃区域显著减弱（ρ≈0.30），与积分函数分解为视角无关和视角相关成分的预测一致。通过残差打乱干预破坏跨视角对齐，同时保留清晰图像，导致非玻璃区域的真值代价下降33%，玻璃区域基于方差的胜者通吃准确率下降4.3倍，证实了该结构函数作为匹配线索的作用。该信号仅存在于MC渲染数据中，构成潜在的模拟到现实迁移的捷径，其对训练网络的影响仍有待量化。
原文摘要 · Abstract (English)
Path-traced synthetic stereo data underlie a large fraction of modern disparity-estimation training pipelines. We report a previously unrecognised property of such data: while the Monte Carlo (MC) noise streams of the two cameras are statistically independent, the underlying \emph{variance fields} -- deterministic per-pixel functions of the rendering integrand -- are highly correlated once aligned by the ground-truth disparity warp. Across 20 scenes rendered with Mitsuba~3, the warped Pearson correlation reaches $ρ{=}0.754{\pm}0.016$ across 20 scenes at $\mathrm{SPP}{=}512$, and on a representative scene remains essentially invariant ($ρ{=}0.778{\pm}0.001$) over a $16\times$ range of samples per pixel. The effect is strongest in Lambertian regions ($ρ{\approx}0.78$) and substantially weaker in glass ($ρ{\approx}0.30$), as predicted by an integrand decomposition into view-independent and view-dependent components. A residual-shuffle intervention that breaks the cross-view alignment while preserving the clean image degrades the GT cost margin by $33\%$ on non-glass and the variance-based winner-take-all accuracy on glass by $4.3\times$, confirming the structure functions as a matching cue. This signal is unique to MC-rendered data and constitutes a candidate sim-to-real shortcut whose impact on trained networks remains to be quantified.
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