1 files changed, 24 insertions, 6 deletions
diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md
index e56b022..49086ca 100644
--- a/doc/CNN_V2.md
+++ b/doc/CNN_V2.md
@@ -245,12 +245,28 @@ fn pack_channels(values: vec4<f32>) -> vec4<u32> {
 **Static Feature Extraction:**
 
 ```python
-def compute_static_features(rgb, depth):
-    """Generate parametric features (8D: p0-p3 + spatial)."""
+def compute_static_features(rgb, depth, mip_level=0):
+    """Generate parametric features (8D: p0-p3 + spatial).
+
+    Args:
+        mip_level: 0=original, 1=half res, 2=quarter res, 3=eighth res
+    """
     h, w = rgb.shape[:2]
 
-    # Parametric features (example: use input RGBD, but could be mips/gradients)
-    p0, p1, p2, p3 = rgb[..., 0], rgb[..., 1], rgb[..., 2], depth
+    # Generate mip level for p0-p3 (downsample then upsample)
+    if mip_level > 0:
+        mip_rgb = rgb.copy()
+        for _ in range(mip_level):
+            mip_rgb = cv2.pyrDown(mip_rgb)
+        for _ in range(mip_level):
+            mip_rgb = cv2.pyrUp(mip_rgb)
+        if mip_rgb.shape[:2] != (h, w):
+            mip_rgb = cv2.resize(mip_rgb, (w, h), interpolation=cv2.INTER_LINEAR)
+    else:
+        mip_rgb = rgb
+
+    # Parametric features from mip level
+    p0, p1, p2, p3 = mip_rgb[..., 0], mip_rgb[..., 1], mip_rgb[..., 2], depth
 
     # UV coordinates (normalized)
     uv_x = np.linspace(0, 1, w)[None, :].repeat(h, axis=0)
@@ -308,6 +324,7 @@ class CNNv2(nn.Module):
 # Hyperparameters
 kernel_sizes = [3, 3, 3]     # Per-layer kernel sizes (e.g., [1,3,5])
 num_layers = 3               # Number of CNN layers
+mip_level = 0                # Mip level for p0-p3: 0=orig, 1=half, 2=quarter, 3=eighth
 learning_rate = 1e-3
 batch_size = 16
 epochs = 5000
@@ -318,8 +335,8 @@ epochs = 5000
 # Training loop (standard PyTorch f32)
 for epoch in range(epochs):
     for rgb_batch, depth_batch, target_batch in dataloader:
-        # Compute static features (8D)
-        static_feat = compute_static_features(rgb_batch, depth_batch)
+        # Compute static features (8D) with mip level
+        static_feat = compute_static_features(rgb_batch, depth_batch, mip_level)
 
         # Input RGBD (4D)
         input_rgbd = torch.cat([rgb_batch, depth_batch.unsqueeze(1)], dim=1)
@@ -342,6 +359,7 @@ torch.save({
     'config': {
         'kernel_sizes': [3, 3, 3],  # Per-layer kernel sizes
         'num_layers': 3,
+        'mip_level': 0,             # Mip level used for p0-p3
         'features': ['p0', 'p1', 'p2', 'p3', 'uv.x', 'uv.y', 'sin10_x', 'bias']
     },
     'epoch': epoch,