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Replace raw shadow (ch18) with dif = max(0,dot(normal,KEY_LIGHT))*shadow
across all layers. Channel count stays 20, weight shapes unchanged.
- gbuf_pack.wgsl: t1.z = pack4x8unorm(mip2.g, mip2.b, dif, transp); t1.w = 0u
- gbuf_deferred.wgsl: read dif from unpack4x8unorm(t1.z).z
- gbuf_view.wgsl: revert to 4×5 grid, ch18=dif label, ch19=trns label
- tools/shaders.js: FULL_PACK_SHADER adds oct_decode + computes dif
- cnn_v3_utils.py: assemble_features() computes dif on-the-fly via oct_decode
- docs: CNN_V3.md, HOWTO.md, HOW_TO_CNN.md, GBUF_DIF_MIGRATION.md updated
handoff(Gemini): shadow→dif migration done, ready for first training pass
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- CNN_V3.md: status line, architecture channel counts (8/16→4/8), FiLM MLP
output count (96→40 params), size budget table (real implemented values)
- HOWTO.md: Phase status table (5→done, add phase 6 training TODO), sections
3-5 rewritten to reflect what exists vs what is still planned
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- CNN_V3.md: complete design document
- U-Net enc_channels=[4,8], ~5 KB f16 weights
- FiLM conditioning (5D → γ/β per level, CPU-side MLP)
- 20-channel feature buffer, 32 bytes/pixel: two rgba32uint textures
- feat_tex0: albedo.rgb, normal.xy, depth, depth_grad.xy (f16)
- feat_tex1: mat_id, prev.rgb, mip1.rgb, mip2.rgb, shadow, transp (u8)
- 4-pass G-buffer: raster MRT + SDF compute + lighting + pack
- Per-pixel parity framework: PyTorch / HTML WebGPU / C++ WebGPU (≤1/255)
- Training pipelines: Blender full G-buffer + photo-only (channel dropout)
- train_cnn_v3_full.sh spec (modelled on v2 script)
- HTML tool adaptation plan from cnn_v2/tools/cnn_v2_test/index.html
- Binary format v3 header spec
- 8-phase ordered implementation checklist
- TODO.md: add CNN v3 U-Net+FiLM future task with phases
- cnn_v3/README.md: update status to design phase
handoff(Gemini): CNN v3 design complete. Phase 0 (stub G-buffer) unblocks
all other phases — one compute shader writing feat_tex0+feat_tex1 with
synthetic values from the current framebuffer. See cnn_v3/docs/CNN_V3.md
Implementation Checklist.
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