docs(cnn_v3): full design doc — U-Net + FiLM architecture plan

- 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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