# Fact Cards

## Fact #1
- **Statement**: LiteSAM achieves RMSE@30 = 17.86m on UAV-VisLoc dataset with average hit rates of 66.66% (Easy), 65.37% (Moderate), 61.65% (Hard) at 83.79ms inference on RTX 3090 at 1184×1184 resolution.
- **Source**: Source #1 (Table 3)
- **Phase**: Assessment
- **Target Audience**: UAV satellite-aerial matching
- **Confidence**: ✅ High
- **Related Dimension**: Accuracy, Speed

## Fact #2
- **Statement**: LiteSAM opt. (without dual softmax) achieves 60.97ms on RTX 3090 with hit rates of 65.09% (Easy), 61.34% (Moderate), 46.16% (Hard). The accuracy drop in Hard mode is significant: from 61.65% to 46.16%.
- **Source**: Source #1 (Table 3)
- **Phase**: Assessment
- **Target Audience**: UAV satellite-aerial matching
- **Confidence**: ✅ High
- **Related Dimension**: Speed vs accuracy trade-off

## Fact #3
- **Statement**: EfficientLoFTR achieves RMSE@30 = 17.87m on UAV-VisLoc with hit rates of 65.78% (Easy), 63.62% (Moderate), 57.65% (Hard) at 112.60ms on RTX 3090 at 1184×1184.
- **Source**: Source #1 (Table 3)
- **Phase**: Assessment
- **Target Audience**: UAV satellite-aerial matching
- **Confidence**: ✅ High
- **Related Dimension**: Accuracy, Speed

## Fact #4
- **Statement**: SuperPoint+LightGlue achieves hit rates of 60.34% (Easy), 59.57% (Moderate), 54.32% (Hard) at 44.15ms on RTX 3090 on UAV-VisLoc. RMSE@30 = 17.81m.
- **Source**: Source #1 (Table 3)
- **Phase**: Assessment
- **Target Audience**: UAV satellite-aerial matching
- **Confidence**: ✅ High
- **Related Dimension**: Accuracy, Speed

## Fact #5
- **Statement**: LiteSAM has 6.31M parameters (2.4x fewer than EfficientLoFTR's 15.05M). FLOPs: LiteSAM 588.51G vs EfficientLoFTR 1036.61G on self-made dataset (1184×1184).
- **Source**: Source #1 (Table 4)
- **Phase**: Assessment
- **Target Audience**: Resource-constrained deployment
- **Confidence**: ✅ High
- **Related Dimension**: VRAM, Computational cost

## Fact #6
- **Statement**: LiteSAM on NVIDIA Jetson AGX Orin (50W) achieves 497.49ms (opt.) at 1184×1184 with AMP. This is 19.8% faster than EfficientLoFTR opt.
- **Source**: Source #1 (Table 10)
- **Phase**: Assessment
- **Target Audience**: Edge device deployment
- **Confidence**: ✅ High
- **Related Dimension**: Edge performance

## Fact #7
- **Statement**: LiteSAM GitHub repository has 5 stars, 0 forks, 4 commits. No releases published. Pretrained weights available via Google Drive link only. No ONNX/TensorRT export support.
- **Source**: Source #2
- **Phase**: Assessment
- **Target Audience**: Production deployment
- **Confidence**: ✅ High
- **Related Dimension**: Maturity, Deployment readiness

## Fact #8
- **Statement**: LiteSAM is built upon EfficientLoFTR (acknowledged in GitHub README). Uses the same coarse-to-fine architecture with MobileOne backbone replacing RepVGG, TAIFormer replacing EfficientLoFTR attention, and MinGRU replacing heatmap-based refinement.
- **Source**: Source #1, Source #2
- **Phase**: Assessment
- **Target Audience**: Architecture evaluation
- **Confidence**: ✅ High
- **Related Dimension**: Architecture, Risk

## Fact #9
- **Statement**: EfficientLoFTR has 964 stars on GitHub, ONNX export available, TensorRT adaptation exists (LoFTR_TRT), HuggingFace integration. Achieves 27ms at 640×480 with mixed precision.
- **Source**: Source #3, Source #5, Source #6
- **Phase**: Assessment
- **Target Audience**: Production deployment
- **Confidence**: ✅ High
- **Related Dimension**: Maturity, Deployment readiness

## Fact #10
- **Statement**: Neither LiteSAM nor EfficientLoFTR are rotation-invariant. This is a known limitation shared with all LoFTR-family matchers. LightGlue is also confirmed not rotation-invariant (GitHub issue #64). Only SIFT provides rotation invariance.
- **Source**: Source #1 (Section 6 Discussion), Source #8
- **Phase**: Assessment
- **Target Audience**: Rotation handling
- **Confidence**: ✅ High
- **Related Dimension**: Rotation handling

## Fact #11
- **Statement**: LiteSAM and EfficientLoFTR are end-to-end matchers that take an image pair and output correspondences. They do NOT perform image retrieval. The DINOv2 coarse retrieval stage is still required to select candidate satellite tiles.
- **Source**: Source #1 (Section 3)
- **Phase**: Assessment
- **Target Audience**: Pipeline architecture
- **Confidence**: ✅ High
- **Related Dimension**: Pipeline integration

## Fact #12
- **Statement**: LiteSAM trained only on MegaDepth (natural image dataset) and generalizes to satellite-aerial without fine-tuning on target domain. However, the paper notes limitations with "significant viewpoint changes or varying resolutions" (Section 6).
- **Source**: Source #1 (Section 6)
- **Phase**: Assessment
- **Target Audience**: Domain generalization
- **Confidence**: ✅ High
- **Related Dimension**: Generalization, Risk

## Fact #13
- **Statement**: On the self-made dataset (Harbin/Qiqihar, 100-500m altitude), LiteSAM achieves RMSE@30=6.12m, HR=92.09/87.88/77.30, 85.31ms. EfficientLoFTR: RMSE=7.28m, HR=90.03/79.79/61.84, 120.72ms. SP+LG: RMSE=6.76m, HR=78.85/70.03/58.31, 49.49ms.
- **Source**: Source #1 (Table 4)
- **Phase**: Assessment
- **Target Audience**: UAV localization at low altitude
- **Confidence**: ✅ High
- **Related Dimension**: Accuracy at low altitude

## Fact #14
- **Statement**: RTX 2060 (Turing) has approximately 40-60% of RTX 3090 deep learning throughput. Estimated LiteSAM inference on RTX 2060: ~140-210ms at 1184×1184. EfficientLoFTR: ~190-280ms. Current SP+LG ONNX FP16: ~130-180ms (per draft03 spec).
- **Source**: General GPU performance knowledge + Source #1 timings
- **Phase**: Assessment
- **Target Audience**: Hardware constraint evaluation
- **Confidence**: ⚠️ Medium (estimates, not measured)
- **Related Dimension**: RTX 2060 performance

## Fact #15
- **Statement**: LiteSAM uses MobileOne-S3 backbone with only 0.81M params after removing classification head. Total model: 6.31M params. VRAM during inference estimated at ~300-500MB for model + feature maps at 1184×1184 resolution.
- **Source**: Source #1 (Section 3.1)
- **Phase**: Assessment
- **Target Audience**: VRAM budget
- **Confidence**: ⚠️ Medium (VRAM estimated, model params confirmed)
- **Related Dimension**: VRAM budget

## Fact #16
- **Statement**: LiteSAM on self-made dataset achieves significantly better Hard mode hit rate (77.30%) compared to EfficientLoFTR (61.84%) and SP+LG (58.31%). This suggests better robustness in difficult matching scenarios.
- **Source**: Source #1 (Table 4)
- **Phase**: Assessment
- **Target Audience**: Difficult matching scenarios
- **Confidence**: ✅ High
- **Related Dimension**: Robustness
