fix: PT-9b - honest variance for unseedable disocclusions; 7x7 borrow#97
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The PT-9 seeding handled ROTATION, where a few pixels per frame stream in at the screen edge next to converged history. Under TRANSLATION - the actual walking-indoors case - a whole COLUMN of texels streams in per frame, the 5x5 borrow window finds nothing but fellow newborns, and the unseeded pixels fell back to the old degenerate state: a raw 1-spp sample with variance EXACTLY zero (m2 - m1 squared of a single value). Zero variance tells the wavelet filter the pixel is CONVERGED, so the raw outliers survived all four a-trous iterations as a visible noise band along the leading screen edge. Two changes, both in the temporal kernel, realtime mode only: - the borrow window grows to 7x7 (newborn pixels only, so the cost is motion-proportional and small); - a newborn with NOTHING to borrow now writes a frank variance (max(l squared, 0.25)) instead of the degenerate zero - a 1-sample estimate has unknown variance, not none - so the a-trous blurs those pixels hard until real statistics exist one frame later. Verified: both golden PT tests pass; the translating-dolly capture (sideways translation, worst-case parallax) shows the noise band gone at unchanged fps; progressive mode untouched. Claude-Session: https://claude.ai/code/session_018574BdCfSjdpNK3WLgx1hP
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📝 WalkthroughWalkthroughThe PT shader’s SVGF accumulation path expands disocclusion neighbor searches from 5×5 to 7×7, tracks successful history seeding, and applies a fallback variance estimate for young unseeded history. ChangesSVGF seeding updates
Estimated code review effort: 2 (Simple) | ~10 minutes Possibly related PRs
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The PT-9 seeding handled ROTATION, where a few pixels per frame stream
in at the screen edge next to converged history. Under TRANSLATION -
the actual walking-indoors case - a whole COLUMN of texels streams in
per frame, the 5x5 borrow window finds nothing but fellow newborns,
and the unseeded pixels fell back to the old degenerate state: a raw
1-spp sample with variance EXACTLY zero (m2 - m1 squared of a single
value). Zero variance tells the wavelet filter the pixel is CONVERGED,
so the raw outliers survived all four a-trous iterations as a visible
noise band along the leading screen edge.
Two changes, both in the temporal kernel, realtime mode only:
motion-proportional and small);
(max(l squared, 0.25)) instead of the degenerate zero - a 1-sample
estimate has unknown variance, not none - so the a-trous blurs those
pixels hard until real statistics exist one frame later.
Verified: both golden PT tests pass; the translating-dolly capture
(sideways translation, worst-case parallax) shows the noise band gone
at unchanged fps; progressive mode untouched.
Claude-Session: https://claude.ai/code/session_018574BdCfSjdpNK3WLgx1hP
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