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# 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**
```bash
ylff build-dataset --sequences-dir data/raw --max-samples 1000
```
3. **Fine-Tune Model**
```bash
ylff train --training-set data/training/training_set.pkl --epochs 10
```
4. **Evaluate**
```bash
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.
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