CNN v2: parametric static features - Phases 1-4

Infrastructure for enhanced CNN post-processing with 7D feature input.

Phase 1: Shaders
- Static features compute (RGBD + UV + sin10_x + bias → 8×f16)
- Layer template (convolution skeleton, packing/unpacking)
- 3 mip level support for multi-scale features

Phase 2: C++ Effect
- CNNv2Effect class (multi-pass architecture)
- Texture management (static features, layer buffers)
- Build integration (CMakeLists, assets, tests)

Phase 3: Training Pipeline
- train_cnn_v2.py: PyTorch model with static feature concatenation
- export_cnn_v2_shader.py: f32→f16 quantization, WGSL generation
- Configurable architecture (kernels, channels)

Phase 4: Validation
- validate_cnn_v2.sh: End-to-end pipeline
- Checkpoint → shaders → build → test images

Tests: 36/36 passing

Next: Complete render pipeline implementation (bind groups, multi-pass)

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

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diff --git a/doc/HOWTO.md b/doc/HOWTO.md
index 2b896ab..7be5246 100644
--- a/doc/HOWTO.md
+++ b/doc/HOWTO.md
@@ -130,6 +130,32 @@ Processes entire image with sliding window (matches WGSL):
 
 **Kernel sizes:** 3×3 (36 weights), 5×5 (100 weights), 7×7 (196 weights)
 
+### CNN v2 Training
+
+Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding + bias):
+
+```bash
+# Train CNN v2 with default config (1×1, 3×3, 5×5 kernels, 16→8→4 channels)
+./training/train_cnn_v2.py \
+  --input training/input/ --target training/output/ \
+  --epochs 5000 --batch-size 16 \
+  --checkpoint-every 1000
+
+# Custom architecture (smaller for size optimization)
+./training/train_cnn_v2.py \
+  --input training/input/ --target training/output/ \
+  --kernel-sizes 1 3 3 --channels 8 4 4 \
+  --epochs 5000 --batch-size 16
+```
+
+**Export shaders:**
+```bash
+./training/export_cnn_v2_shader.py checkpoints/checkpoint_epoch_5000.pth \
+  --output-dir workspaces/main/shaders
+```
+
+Generates `cnn_v2_layer_0.wgsl`, `cnn_v2_layer_1.wgsl`, `cnn_v2_layer_2.wgsl` with f16 weights.
+
 ### CNN v2 Validation
 
 End-to-end testing: checkpoint → shaders → build → test images → results