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Diffstat (limited to 'doc/CNN_V2.md')
| -rw-r--r-- | doc/CNN_V2.md | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md index 49086ca..a66dc1d 100644 --- a/doc/CNN_V2.md +++ b/doc/CNN_V2.md @@ -11,14 +11,15 @@ CNN v2 extends the original CNN post-processing effect with parametric static fe **Key improvements over v1:** - 7D static feature input (vs 4D RGB) - Multi-frequency position encoding (NeRF-style) +- Configurable mip-level for p0-p3 parametric features (0-3) - Per-layer configurable kernel sizes (1×1, 3×3, 5×5) - Variable channel counts per layer - Float16 weight storage (~3.2 KB for 3-layer model) - Bias integrated as static feature dimension - Storage buffer architecture (dynamic layer count) -- Binary weight format for runtime loading +- Binary weight format v2 for runtime loading -**Status:** ✅ Complete. Training pipeline functional, validation tools ready. +**Status:** ✅ Complete. Training pipeline functional, validation tools ready, mip-level support integrated. **TODO:** 8-bit quantization with QAT for 2× size reduction (~1.6 KB) --- @@ -109,12 +110,12 @@ Input RGBD → Static Features Compute → CNN Layers → Output RGBA ```wgsl // Slot 0-3: Parametric features (p0, p1, p2, p3) -// Can be: mip1/2 RGBD, grayscale, gradients, etc. -// Distinct from input image RGBD (fed only to Layer 0) -let p0 = ...; // Parametric feature 0 (e.g., mip1.r or grayscale) -let p1 = ...; // Parametric feature 1 -let p2 = ...; // Parametric feature 2 -let p3 = ...; // Parametric feature 3 +// Sampled from configurable mip level (0=original, 1=half, 2=quarter, 3=eighth) +// Training sets mip_level via --mip-level flag, stored in binary format v2 +let p0 = ...; // RGB.r from selected mip level +let p1 = ...; // RGB.g from selected mip level +let p2 = ...; // RGB.b from selected mip level +let p3 = ...; // Depth or RGB channel from mip level // Slot 4-5: UV coordinates (normalized screen space) let uv_x = coord.x / resolution.x; // Horizontal position [0,1] |