Independent Samples, Correlated Variance A Learnable Cross-View Cue in Path-Traced Stereo Data

Path-traced synthetic stereo is a primary training substrate for disparity networks, and the pipelines that consume it assume Monte Carlo (MC) rendering noise is independent across the two views. The assumption is correct at the level it is stated—individual samples—but silent about the object a network actually sees. We show that the per-pixel MC variance fields, though built from independent samples, are strongly correlated once aligned by the ground-truth disparity, and that a network can learn to match with that correlation. Across 20 indoor scenes the warped correlation is 0.754 ± 0.016 against 0.360 unwarped; it replicates on a second renderer with a different sampler and sample budget (0.743 ± 0.044), and a seed-count analysis puts the population value near 0.85, making the measurement a lower bound. The effect is field-level, not sample-level: at the warp correspondence the signed per-seed residual correlation is -0.0004 while the residual envelope correlates at 0.38. A capacity-limited siamese probe given nothing but variance-field patches reaches 78.8% two-alternative forced-choice accuracy on held-out scenes and falls to chance once the alignment is destroyed; under the ordinary single-render condition, decorrelation still costs 2.40 percentage points in a difference-in-differences design, positive in all six held-out scenes. A real sensor's variance is fixed by its own signal rather than by transport difficulty, so across views it is redundant with intensity: the cue is specific to rendered data.

• path tracing
• Monte Carlo rendering
• stereo matching
• variance correlation
• sim-to-real gap
• learnable shortcut
• Mitsuba 3