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C++ GBufViewEffect: renders all 20 feature channels from feat_tex0/feat_tex1
in a 4×5 tiled grid. Custom BGL with WGPUTextureSampleType_Uint; bind group
rebuilt per frame via wgpuRenderPipelineGetBindGroupLayout.
Web tool: "Load sample directory" button — webkitdirectory picker, FULL_PACK_SHADER
compute (matches gbuf_pack.wgsl packing), runFromFeat() skips photo-pack step,
computePSNR() readback + comparison vs target.png side-by-side.
36/36 tests pass. Docs updated: HOWTO.md §9, README, PROJECT_CONTEXT, TODO,
COMPLETED.
handoff(Gemini): CNN v3 Phase 7 done. Next: run train_cnn_v3.py (see HOWTO §3).
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- Wire GBufferEffect into demo build: assets.txt, DemoSourceLists.cmake,
demo_effects.h, shaders.h/cc. ShaderComposer::Compose() applied to
gbuf_raster.wgsl (resolves #include "common_uniforms").
- Add GBufferEffect construction test. 35/35 passing.
- Write cnn_v3/docs/HOWTO.md: G-buffer wiring, training data prep,
training plan, per-pixel validation workflow, phase status table,
troubleshooting guide.
- Add project hooks: remind to update HOWTO.md on cnn_v3/ edits;
warn on direct str_view(*_wgsl) usage bypassing ShaderComposer.
- Update PROJECT_CONTEXT.md and TODO.md: Phase 1 done,
Phase 3 (WGSL U-Net shaders) is next active.
handoff(Gemini): CNN v3 Phase 3 is next - WGSL enc/dec/bottleneck/FiLM
shaders in cnn_v3/shaders/. See cnn_v3/docs/CNN_V3.md Architecture
section and cnn_v3/docs/HOWTO.md section 3 for spec. GBufferEffect
outputs feat_tex0 + feat_tex1 (rgba32uint, 20ch, 32 bytes/pixel).
C++ CNNv3Effect (Phase 4) takes those as input nodes.
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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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Initialize CNN v3 subdirectory with training pipeline layout:
- docs/, scripts/, shaders/, src/, tools/, weights/ for organization
- training/input/ with sample images
- training/target_1/, target_2/ for multi-style training
- README.md documenting structure
Training images tracked in repo for easy collaboration.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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