Cross-View Variance Correlation in Path-Traced Stereo:A Hidden Shortcut in Synthetic Training Data

• URL: http://arxiv.org/abs/2606.25483v1
• Date: Wed, 24 Jun 2026 07:13:00 GMT
• Title: Cross-View Variance Correlation in Path-Traced Stereo:A Hidden Shortcut in Synthetic Training Data
• Abstract summary: Path-traced synthetic stereo data underlie a large fraction of disparity-estimation training pipelines.<n>We show that the underlying emphvariance fields are highly correlated once aligned by the ground-truth disparity warp.<n>This signal is unique to MC-rendered data and constitutes a candidate sim-to-real shortcut.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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