refactor: Optimize CNN grayscale computation

Compute gray once per fragment using dot() instead of per-layer.
Pass gray as f32 parameter to conv functions instead of vec4 original.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

1 files changed, 6 insertions, 4 deletions

diff --git a/doc/CNN_RGBD_GRAYSCALE_SUMMARY.md b/doc/CNN_RGBD_GRAYSCALE_SUMMARY.md
index 4c13693..3439f2c 100644
--- a/doc/CNN_RGBD_GRAYSCALE_SUMMARY.md
+++ b/doc/CNN_RGBD_GRAYSCALE_SUMMARY.md
@@ -20,7 +20,7 @@ Implemented CNN architecture upgrade: RGBD input → grayscale output with 7-cha
 
 - **RGBD:** `(rgbd - 0.5) * 2`
 - **UV coords:** `(uv - 0.5) * 2`
-- **Grayscale:** `(0.2126*R + 0.7152*G + 0.0722*B - 0.5) * 2`
+- **Grayscale:** `dot(original.rgb, vec3<f32>(0.2126, 0.7152, 0.0722))` (computed once, passed as parameter)
 
 **Rationale:** Zero-centered inputs for tanh activation, better gradient flow.
 
@@ -48,13 +48,14 @@ Implemented CNN architecture upgrade: RGBD input → grayscale output with 7-cha
 
 **Shaders (`/Users/skal/demo/workspaces/main/shaders/cnn/cnn_conv3x3.wgsl`):**
 1. Added `cnn_conv3x3_7to4()`:
-   - 7-channel input: [RGBD, uv_x, uv_y, gray]
+   - 7-channel input: [RGBD, uv_x, uv_y, gray] (gray passed as parameter)
    - 4-channel output: RGBD
    - Weights: `array<array<f32, 8>, 36>`
 2. Added `cnn_conv3x3_7to1()`:
-   - 7-channel input: [RGBD, uv_x, uv_y, gray]
+   - 7-channel input: [RGBD, uv_x, uv_y, gray] (gray passed as parameter)
    - 1-channel output: grayscale
    - Weights: `array<array<f32, 8>, 9>`
+3. Optimized: gray computed once in caller using `dot()`, not per-function
 
 **Documentation (`/Users/skal/demo/doc/CNN_EFFECT.md`):**
 1. Updated architecture section with RGBD→grayscale pipeline
@@ -71,7 +72,8 @@ CNNLayerParams and bind groups remain unchanged.
 2. Each layer:
    - Samples previous layer output (RGBD in [0,1])
    - Normalizes RGBD to [-1,1]
-   - Computes UV coords and grayscale, normalizes to [-1,1]
+   - Computes gray once using `dot()` (fs_main level)
+   - Normalizes UV coords to [-1,1] (inside conv functions)
    - Concatenates 7-channel input
    - Applies convolution with layer-specific weights
    - Outputs RGBD (inner) or grayscale (final) in [-1,1]