# Fact Cards

## Fact #1
- **Statement**: Draft04 uses SuperPoint+LightGlue for VO (150-200ms/frame) while draft03 used XFeat (15ms/frame). This 10x speed regression was NOT listed in draft04's assessment findings — it appears to be an unintentional change.
- **Source**: [Source #3] XFeat paper, draft03 vs draft04 comparison
- **Phase**: Assessment
- **Target Audience**: GPS-denied UAV system
- **Confidence**: ✅ High
- **Related Dimension**: VO Matcher Selection

## Fact #2
- **Statement**: XFeat outperforms SuperPoint on Megadepth: AUC@10° 65.4 vs 50.1, with more inliers (892 vs 495). For high-overlap consecutive frames (60-80%), XFeat quality is sufficient.
- **Source**: [Source #3] XFeat paper Table 1
- **Phase**: Assessment
- **Target Audience**: UAV VO pipeline
- **Confidence**: ✅ High
- **Related Dimension**: VO Matcher Quality

## Fact #3
- **Statement**: SatLoc-Fusion (2025) validates XFeat for UAV VO in a similar setup: nadir camera, 100-300m altitude, <15m error, >90% trajectory coverage, >2Hz on 6 TFLOPS edge hardware.
- **Source**: [Source #4] SatLoc-Fusion
- **Phase**: Assessment
- **Target Audience**: UAV VO pipeline
- **Confidence**: ✅ High
- **Related Dimension**: VO Matcher Selection

## Fact #4
- **Statement**: LiteSAM's 77.3% Hard hit rate is on the authors' SELF-MADE dataset (Harbin/Qiqihar, 100-500m altitude), NOT UAV-VisLoc. On UAV-VisLoc Hard, LiteSAM achieves 61.65% hit rate with RMSE@30=17.86m.
- **Source**: [Source #2] LiteSAM paper
- **Phase**: Assessment
- **Target Audience**: Satellite-aerial matching
- **Confidence**: ✅ High
- **Related Dimension**: Satellite Matching Accuracy

## Fact #5
- **Statement**: LiteSAM GitHub repo has 5 stars, 0 forks, 4 commits, no releases, no license, no issues. Single maintainer. Very low community adoption.
- **Source**: [Source #1] LiteSAM GitHub
- **Phase**: Assessment
- **Target Audience**: Production readiness evaluation
- **Confidence**: ✅ High
- **Related Dimension**: LiteSAM Maturity

## Fact #6
- **Statement**: LiteSAM weights are hosted on Google Drive as a single .ckpt file (mloftr.ckpt) with no checksum, no mirror, no alternative download source.
- **Source**: [Source #1] LiteSAM GitHub
- **Phase**: Assessment
- **Target Audience**: Supply chain security
- **Confidence**: ✅ High
- **Related Dimension**: Security

## Fact #7
- **Statement**: CVE-2025-32434 allows RCE even with weights_only=True in torch.load() (PyTorch ≤2.5.1). CVE-2026-24747 shows memory corruption in the weights_only unpickler (fixed in PyTorch ≥2.10.0).
- **Source**: [Source #5, #6] NVD
- **Phase**: Assessment
- **Target Audience**: All PyTorch-based systems
- **Confidence**: ✅ High
- **Related Dimension**: Security

## Fact #8
- **Statement**: EfficientLoFTR (LiteSAM's base) has 964 stars, HuggingFace integration, CVPR 2024 publication. 15.05M params. Much more mature and proven than LiteSAM.
- **Source**: [Source #10] EfficientLoFTR GitHub
- **Phase**: Assessment
- **Target Audience**: Satellite-aerial matching fallback
- **Confidence**: ✅ High
- **Related Dimension**: LiteSAM Maturity

## Fact #9
- **Statement**: LiteSAM has no ONNX or TensorRT export path. EfficientLoFTR also lacks official ONNX support. Custom conversion work would be required.
- **Source**: [Source #1, #10] GitHub repos
- **Phase**: Assessment
- **Target Audience**: Performance optimization
- **Confidence**: ✅ High
- **Related Dimension**: Performance

## Fact #10
- **Statement**: LiteSAM was benchmarked on RTX 3090. Performance on RTX 2060 is estimated at ~140-210ms but not measured. RTX 2060 has ~22% of RTX 3090 FP32 throughput.
- **Source**: [Source #2] LiteSAM paper + GPU specs
- **Phase**: Assessment
- **Target Audience**: RTX 2060 deployment
- **Confidence**: ⚠️ Medium (extrapolated)
- **Related Dimension**: Performance

## Fact #11
- **Statement**: DINOv2 ViT-S/14 uses ~300MB VRAM; ViT-B/14 uses ~900-1100MB VRAM (3-4x more). ViT-B provides +2.54pp recall improvement over ViT-S.
- **Source**: [Source #7] Nature Scientific Reports
- **Phase**: Assessment
- **Target Audience**: VRAM budget
- **Confidence**: ⚠️ Medium (extrapolated from classification task)
- **Related Dimension**: DINOv2 Model Selection

## Fact #12
- **Statement**: Google Maps intentionally does not publish recent satellite imagery for conflict areas in Ukraine. Imagery is typically 1-3 years old. Google stated: "These satellite images were taken more than a year ago."
- **Source**: [Source #8] AIN.ua, Google statements
- **Phase**: Assessment
- **Target Audience**: Satellite imagery freshness
- **Confidence**: ✅ High
- **Related Dimension**: Satellite Imagery Quality

## Fact #13
- **Statement**: GTSAM iSAM2.update() can throw IndeterminantLinearSystemException with certain factor configurations. Long chains (3000 frames) should work via Bayes tree structure but need error handling.
- **Source**: [Source #9] GTSAM GitHub #561
- **Phase**: Assessment
- **Target Audience**: Factor graph robustness
- **Confidence**: ✅ High
- **Related Dimension**: GTSAM Robustness

## Fact #14
- **Statement**: No independent reproduction of LiteSAM results exists. Search results often confuse LiteSAM (feature matcher) with Lite-SAM (ECCV 2024 segmentation model).
- **Source**: [Source #1] Research verification
- **Phase**: Assessment
- **Target Audience**: Production readiness
- **Confidence**: ✅ High
- **Related Dimension**: LiteSAM Maturity

## Fact #15
- **Statement**: With XFeat for VO (~200MB VRAM) instead of SuperPoint+LightGlue (~900MB), peak VRAM drops from ~1.6GB to ~900MB (XFeat 200 + DINOv2 300 + LiteSAM 400).
- **Source**: Calculated from Sources #1, #3, #7
- **Phase**: Assessment
- **Target Audience**: VRAM budget
- **Confidence**: ⚠️ Medium (estimated)
- **Related Dimension**: VRAM Budget

## Fact #16
- **Statement**: Maxar restored satellite imagery access for Ukraine in March 2025 (was suspended). Commercial, paid-only. 31-50cm resolution (WorldView, GeoEye).
- **Source**: [Source #12] Defense Express
- **Phase**: Assessment
- **Target Audience**: Alternative satellite providers
- **Confidence**: ✅ High
- **Related Dimension**: Satellite Imagery Quality

## Fact #17
- **Statement**: Tracasa offers free super-resolved Sentinel-2 imagery for Ukraine at 2.5m resolution (500,000 km²). Deep learning upscale from 10m. Could serve as emergency fallback but resolution is insufficient for primary matching.
- **Source**: [Source #11] Tracasa
- **Phase**: Assessment
- **Target Audience**: Alternative satellite sources
- **Confidence**: ⚠️ Medium (2.5m resolution vs required 0.3-0.5m)
- **Related Dimension**: Satellite Imagery Quality
