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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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