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dfWithID() in gbuf_shadow.wgsl now branches on obj.params.x
(ObjectType) instead of using sdBox for everything:
0=CUBE → sdBox(lp, vec3(1))
1=SPHERE → sdSphere(lp, 1.0)
2=PLANE → sdPlane(lp, vec3(0,1,0), obj.params.y)
3=TORUS → sdTorus(lp, vec2(0.8, 0.2))
36/36 tests pass.
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Implements gbuf_shadow.wgsl: fullscreen render pass that reads depth
from Pass 1, reconstructs world-space positions, evaluates a proxy-box
SDF for each object (via inv_model), computes soft shadows for both
directional lights using shadowWithStoredDistance(), and writes shadow
factor to the RGBA8Unorm node_shadow_ target consumed by gbuf_pack.wgsl.
Bind layout: B0=GlobalUniforms, B1=ObjectsBuffer (storage-read),
B2=texture_depth_2d, B3=GBufLightsUniforms.
Sky fragments (depth=1.0) are output as 1.0 (fully lit).
Falls back to clear(1.0) if pipeline is not ready.
36/36 tests pass.
handoff(Gemini): Pass 2 done. Pass 3 (transparency) still TODO.
Phase 4 (type-aware SDF) optional after visual validation.
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GBufferEffect:
- set_scene() now owns Scene/Camera internally; no external pointers needed
- 20 randomly rotating cubes (xorshift32 seed, axis-angle animation)
- 4 pumping spheres (radius = base_r * (1 + audio_intensity * 0.8))
- Camera at (0,2.5,6) looking at origin; aspect updated per-frame
- GBufLightsUniforms: 2 directional lights (warm key + cool fill)
- object_type written to ObjectData.params.x (ready for SDF shadow)
- shadow/transp nodes cleared via zero-draw render passes (placeholder)
- bilinear sampler cached via create_linear_sampler() / sampler_.get()
- dead placeholder textures removed
GBufViewEffect:
- gbuf_view.wgsl: all channels now fully grayscale (removed color tint)
- seq_compiler.py: GBufViewEffect added to CLASS_TO_HEADER
- timeline.seq: cnn_v3_test uses GBufViewEffect -> sink for debug view
Docs: HOWTO.md §1 updated with set_scene() description + §1b implementation
plan for Pass 2 SDF shadow (shader spec, bind layout, C++ additions)
handoff(Gemini): GBufferEffect has internal scene, 36/36 tests green.
Next: implement Pass 2 shadow (gbuf_shadow.wgsl) per §1b plan in HOWTO.md.
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WebGPU auto-reflects the BGL from the shader; a declared-but-unused sampler
binding is omitted from the layout, causing CreateBindGroup to reject it.
Removed binding 6 (sampler) entirely — all reads use textureLoad(). Renumbered
f0/f1 from 7/8 to 6/7 to match.
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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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- train_cnn_v3.py: CNNv3 U-Net+FiLM model, training loop, CLI
- cnn_v3_utils.py: image I/O, pyrdown, depth_gradient, assemble_features,
apply_channel_dropout, detect_salient_points, CNNv3Dataset
- Patch-based training (default 64×64) with salient-point extraction
(harris/shi-tomasi/fast/gradient/random detectors, pre-cached at init)
- Channel dropout for geometric/context/temporal channels
- Random FiLM conditioning per sample for joint MLP+U-Net training
- docs: HOWTO.md §3 updated with commands and flag reference
- TODO.md: Phase 6 marked done, export script noted as next step
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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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/src/cnn_v3_effect.{h,cc}: full Effect subclass with 5 compute
passes (enc0→enc1→bottleneck→dec1→dec0), shared weights storage buffer,
per-pass uniform buffers, set_film_params() API
- Fixed WGSL/C++ struct alignment: vec3u has align=16, so CnnV3Params4ch
is 64 bytes and CnnV3ParamsEnc1 is 96 bytes (not 48/80)
- Weight offsets computed as explicit formulas (e.g. 20*4*9+4) for clarity
- Registered in CMake, shaders.h/cc, demo_effects.h, test_demo_effects.cc
- 35/35 tests pass
handoff(Gemini): CNN v3 Phase 5 next — parity validation (Python ref vs WGSL)
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5 compute shaders + cnn_v3/common snippet:
enc0: Conv(20→4,3×3) + FiLM + ReLU full-res
enc1: AvgPool + Conv(4→8,3×3) + FiLM + ReLU half-res
bottleneck: AvgPool + Conv(8→8,1×1) + ReLU quarter-res
dec1: NearestUp + cat(enc1) + Conv(16→4) + FiLM half-res
dec0: NearestUp + cat(enc0) + Conv(8→4) + FiLM + Sigmoid full-res
Parity rules: zero-pad conv, AvgPool down, NearestUp, FiLM after
conv+bias, skip=concat, OIHW weights+bias layout. Matches PyTorch
train_cnn_v3.py forward() exactly.
Registered in workspaces/main/assets.txt + src/effects/shaders.cc.
Weight layout + Params struct documented in cnn_v3/docs/HOWTO.md §7.
Next: Phase 4 — C++ CNNv3Effect + FiLM uniform upload.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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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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