# Comparison Framework

## Selected Framework Type
Decision Support — comparing three satellite fine matching approaches for production deployment.

## Selected Dimensions
1. Accuracy (RMSE, Hit Rate)
2. Inference speed (RTX 3090 measured, RTX 2060 estimated)
3. VRAM footprint
4. Rotation handling
5. Codebase maturity & deployment readiness
6. Pipeline integration complexity
7. Risk assessment
8. Cost of adoption

## Comparison Table

| Dimension | SuperPoint+LightGlue ONNX FP16 (current) | LiteSAM | EfficientLoFTR |
|-----------|-------------------------------------------|---------|----------------|
| **UAV-VisLoc RMSE@30** | 17.81m | 17.86m | 17.87m |
| **UAV-VisLoc HR Easy/Mod/Hard** | 60.34/59.57/54.32% | 66.66/65.37/61.65% | 65.78/63.62/57.65% |
| **Self-made RMSE@30** | 6.76m | 6.12m | 7.28m |
| **Self-made HR Easy/Mod/Hard** | 78.85/70.03/58.31% | 92.09/87.88/77.30% | 90.03/79.79/61.84% |
| **RTX 3090 time (1184×1184)** | ~44ms (sparse) | 83.79ms | 112.60ms |
| **RTX 2060 est. time** | ~130-180ms (ONNX FP16) | ~140-210ms (PyTorch) | ~190-280ms (PyTorch) |
| **Parameters** | ~12M (SP) + ~12M (LG) | 6.31M | 15.05M |
| **VRAM (model)** | ~900MB (SP+LG) | ~300-500MB (est.) | ~600-800MB (est.) |
| **Rotation invariant** | No (SIFT fallback) | No | No |
| **ONNX support** | ✅ LightGlue-ONNX | ❌ None | ⚠️ LoFTR_TRT pattern |
| **TensorRT support** | ✅ Via LightGlue-ONNX | ❌ None | ⚠️ LoFTR_TRT available |
| **FP16 on Turing** | ✅ Verified | ❌ Not tested | ⚠️ Not verified |
| **GitHub stars** | SP: 4.5K, LG: 3.2K | 5 | 964 |
| **Community/Issues** | Active, well-supported | None | Active |
| **HuggingFace** | ✅ | ❌ | ✅ |
| **Training data** | MegaDepth (pretrained) | MegaDepth (pretrained) | MegaDepth (pretrained) |
| **Pipeline change** | None (current) | Replace Stage 2 fine matching | Replace Stage 2 fine matching |
| **Matching type** | Sparse (detect+match) | Semi-dense (end-to-end) | Semi-dense (end-to-end) |
| **Subpixel accuracy** | Via LightGlue refinement | ✅ MinGRU subpixel | ✅ Two-stage correlation |
| **Factual Basis** | Facts #4, #9, #10 | Facts #1, #2, #5-8, #10-16 | Facts #3, #5, #9, #10 |
