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±0.016 across 20 scenes at
SPP=512, and on a representative scene remains essentially invariant (
ρ=0.778±0.001) over a
16× range of samples per pixel. The effect is strongest in Lambertian regions (
ρ≈0.78) and substantially weaker in glass (
ρ≈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×, 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.