docs/IMPLEMENTATION_STATUS.md

YLFF Implementation Status

✅ Completed Components

Core Infrastructure

1. BA Validator (ylff/ba_validator.py)

   • ✅ Feature extraction integration (SuperPoint via hloc)
   • ✅ Feature matching integration (LightGlue)
   • ✅ Pose error computation (geodesic rotation distance)
   • ✅ Trajectory alignment (Procrustes)
   • ✅ Sample categorization (accept/reject-learnable/reject-outlier)
   • ⚠️ COLMAP BA integration (structure in place, needs full triangulation)

2. Data Pipeline (ylff/data_pipeline.py)

   • ✅ Sequence processing
   • ✅ Model inference integration
   • ✅ BA validation integration
   • ✅ Training set building
   • ✅ Statistics tracking
   • ✅ Data saving/loading

3. Fine-Tuning (ylff/fine_tune.py)

   • ✅ Dataset class for BA-supervised samples
   • ✅ Training loop with pose loss
   • ✅ Optimizer and scheduler setup
   • ✅ Checkpoint saving
   • ✅ Progress tracking

4. Loss Functions (ylff/losses.py)

   • ✅ Geodesic rotation loss
   • ✅ Pose loss (rotation + translation)
   • ✅ Depth loss (L1/L2)
   • ✅ Confidence-weighted loss

5. Model Loading (ylff/models.py)

   • ✅ DA3 model loading from HuggingFace
   • ✅ Checkpoint loading utilities

6. Evaluation (ylff/evaluate.py)

   • ✅ BA agreement rate computation
   • ✅ Error metrics collection

7. CLI (ylff/cli.py)

   • ✅ validate command
   • ✅ build-dataset command
   • ✅ train command
   • ✅ evaluate command

8. Configuration (configs/)

   • ✅ BA configuration (ba_config.yaml)
   • ✅ Training configuration (train_config.yaml)

9. Scripts (scripts/)

   • ✅ BA validation batch script
   • ✅ Fine-tuning script
   • ✅ BA pipeline setup script

10. Documentation

    • ✅ README.md
    • ✅ SETUP.md
    • ✅ Example usage script

⚠️ Partial Implementation

COLMAP BA Integration

The _run_colmap_ba method in ba_validator.py has the structure but needs:

• Full point triangulation from matches
• 3D point addition to reconstruction
• Actual bundle adjustment execution
• Reprojection error computation

Note: This is the most complex part and requires deep COLMAP integration. The current implementation provides the framework; full BA can be added incrementally.

📋 Next Steps

Immediate (for testing)

1. Complete COLMAP BA integration

   • Implement point triangulation
   • Add points to reconstruction
   • Run actual bundle adjustment
   • Compute reprojection errors

2. Test with sample data

   • Create minimal test sequence
   • Verify BA validator works
   • Test data pipeline
   • Test fine-tuning loop

For Production Use

1. Data Collection

   • Collect 1000+ ARKit sequences
   • Organize in data/raw/ directory

2. Run BA Validation

   ylff build-dataset --sequences-dir data/raw --max-samples 1000
   

3. Fine-Tune Model

   ylff train --training-set data/training/training_set.pkl --epochs 10
   

4. Evaluate

   ylff evaluate --sequences-dir data/test
   

🔧 Dependencies

Required

• PyTorch 2.0+
• DA3 models (from HuggingFace)
• COLMAP (system installation)
• pycolmap (Python bindings)
• hloc (Hierarchical Localization)
• LightGlue (feature matching)

Optional

• TensorBoard (for training visualization)
• Jupyter (for experimentation)

📝 Notes

1. DA3 Integration: The code assumes DA3 is available via depth_anything_3.api. If DA3 is not installed, models can still be loaded from HuggingFace if the API is available.

2. BA Complexity: Full COLMAP BA requires:

   • Feature extraction and matching (done)
   • Point triangulation (needs implementation)
   • Bundle adjustment (needs implementation)
   • This is the most complex part and may require iterative development.

3. Data Format: The pipeline expects sequences as directories of images. ARKit JSON metadata can be processed separately if needed.

4. GPU Requirements: Fine-tuning requires GPU. BA validation can run on CPU but is slow.

🎯 Success Criteria

The implementation is ready for:

• ✅ Testing with sample sequences
• ✅ Building training datasets
• ✅ Fine-tuning models (once BA is fully integrated)
• ✅ Evaluating improvements

The framework is complete; full functionality requires completing the COLMAP BA integration.