Yunus Balcioglu

Following up on my earlier COP filtering work, I added another high-quality method to OSSI (Output-Space Subpixel Integration) and compared it with the updated Houdini 22 Swirl COP.

H22 is a substantial improvement over H21 in terms of smoothing the swirl lines, but the central singular region is still visibly unstable and poorly resolved. Some broader curved regions also become noticeably softened. This is consistent with Box filtering over a rectangular area footprint estimated from finite differences of the warp, whereas OSSI integrates how the nonlinear deformation varies across the output pixel.

The new method uses adaptive antithetic Owen-scrambled Sobol sampling. Sobol provides low-discrepancy progressive coverage, Owen scrambling changes the sequence per pixel to reduce visible sampling structure, and pairing each offset with its opposite keeps every completed pair exactly centered.

Like my existing Adaptive R2 method, the sample count and integration width respond to the estimated warp difficulty. Adaptive R2 uses a lightweight progressive R2 disk sequence with a hashed per-pixel rotation, while the Owen-Sobol method uses pixel-specific scrambling of the Sobol sequence together with antithetic sample pairs.

The sample budget and integration width are driven by a conservative finite-difference Jacobian envelope and a footprint-scale second-order nonlinearity estimate. Power-of-two pair budgets evaluate the Sobol sequence at its strongest stratification points as sampling increases.

This is a comparison of the complete stock and custom implementations, not an isolated Box-versus-Sobol test, since their center regularization and footprint-planning strategies also differ.

Finally I can retire my python module that does this for a decade

Hi,

If you have multiple pieces to copy selectively, you can use variants: