[Epic] RT denoiser (shadows, AO, GI) #85

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opened 2026-09-27 05:44:58 +00:00 by backspace119 · 0 comments
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Goal: one reusable denoiser for 1-spp ray-traced signals: shadows (per light or aggregated), AO, diffuse GI (and later reflections).

Approach

SVGF / ReBLUR-style: temporal accumulation (reprojection via motion vectors, history length, disocclusion reset from depth+normal) -> variance estimate -> edge-aware à-trous spatial filter guided by the thin G-buffer. Separate signal inputs so AO/shadow/GI can share passes but keep distinct parameters.

Children

  1. Temporal accumulation pass (shared).
  2. Spatial à-trous filter (normal/depth/luminance weights).
  3. Hook RT projector shadows: move shadow evaluation to a per-pixel visibility buffer for the top-N lights (or stochastic light selection) so it can be denoised; lit pass reads the filtered visibility.
  4. Debug views (raw vs denoised, history length).

Size: M-L. Depends on: Temporal epic items 1, 2, 4, 6.

**Goal:** one reusable denoiser for 1-spp ray-traced signals: shadows (per light or aggregated), AO, diffuse GI (and later reflections). ### Approach SVGF / ReBLUR-style: temporal accumulation (reprojection via motion vectors, history length, disocclusion reset from depth+normal) -> variance estimate -> edge-aware à-trous spatial filter guided by the thin G-buffer. Separate signal inputs so AO/shadow/GI can share passes but keep distinct parameters. ### Children 1. Temporal accumulation pass (shared). 2. Spatial à-trous filter (normal/depth/luminance weights). 3. Hook RT projector shadows: move shadow evaluation to a per-pixel visibility buffer for the top-N lights (or stochastic light selection) so it can be denoised; lit pass reads the filtered visibility. 4. Debug views (raw vs denoised, history length). **Size:** M-L. **Depends on:** Temporal epic items 1, 2, 4, 6.
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backspace119/Slipstream#85
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