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add clarification to research methodology by including a step for solution comparison and user consultation
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Oleksandr Bezdieniezhnykh
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# Fact Cards
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## Fact #1
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- **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.
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- **Source**: [Source #3] XFeat paper, draft03 vs draft04 comparison
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- **Phase**: Assessment
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- **Target Audience**: GPS-denied UAV system
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- **Confidence**: ✅ High
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- **Related Dimension**: VO Matcher Selection
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## Fact #2
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- **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.
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- **Source**: [Source #3] XFeat paper Table 1
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- **Phase**: Assessment
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- **Target Audience**: UAV VO pipeline
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- **Confidence**: ✅ High
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- **Related Dimension**: VO Matcher Quality
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## Fact #3
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- **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.
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- **Source**: [Source #4] SatLoc-Fusion
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- **Phase**: Assessment
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- **Target Audience**: UAV VO pipeline
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- **Confidence**: ✅ High
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- **Related Dimension**: VO Matcher Selection
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## Fact #4
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- **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.
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- **Source**: [Source #2] LiteSAM paper
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- **Phase**: Assessment
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- **Target Audience**: Satellite-aerial matching
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- **Confidence**: ✅ High
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- **Related Dimension**: Satellite Matching Accuracy
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## Fact #5
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- **Statement**: LiteSAM GitHub repo has 5 stars, 0 forks, 4 commits, no releases, no license, no issues. Single maintainer. Very low community adoption.
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- **Source**: [Source #1] LiteSAM GitHub
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- **Phase**: Assessment
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- **Target Audience**: Production readiness evaluation
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- **Confidence**: ✅ High
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- **Related Dimension**: LiteSAM Maturity
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## Fact #6
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- **Statement**: LiteSAM weights are hosted on Google Drive as a single .ckpt file (mloftr.ckpt) with no checksum, no mirror, no alternative download source.
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- **Source**: [Source #1] LiteSAM GitHub
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- **Phase**: Assessment
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- **Target Audience**: Supply chain security
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- **Confidence**: ✅ High
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- **Related Dimension**: Security
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## Fact #7
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- **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).
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- **Source**: [Source #5, #6] NVD
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- **Phase**: Assessment
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- **Target Audience**: All PyTorch-based systems
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- **Confidence**: ✅ High
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- **Related Dimension**: Security
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## Fact #8
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- **Statement**: EfficientLoFTR (LiteSAM's base) has 964 stars, HuggingFace integration, CVPR 2024 publication. 15.05M params. Much more mature and proven than LiteSAM.
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- **Source**: [Source #10] EfficientLoFTR GitHub
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- **Phase**: Assessment
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- **Target Audience**: Satellite-aerial matching fallback
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- **Confidence**: ✅ High
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- **Related Dimension**: LiteSAM Maturity
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## Fact #9
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- **Statement**: LiteSAM has no ONNX or TensorRT export path. EfficientLoFTR also lacks official ONNX support. Custom conversion work would be required.
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- **Source**: [Source #1, #10] GitHub repos
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- **Phase**: Assessment
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- **Target Audience**: Performance optimization
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- **Confidence**: ✅ High
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- **Related Dimension**: Performance
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## Fact #10
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- **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.
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- **Source**: [Source #2] LiteSAM paper + GPU specs
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- **Phase**: Assessment
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- **Target Audience**: RTX 2060 deployment
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- **Confidence**: ⚠️ Medium (extrapolated)
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- **Related Dimension**: Performance
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## Fact #11
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- **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.
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- **Source**: [Source #7] Nature Scientific Reports
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- **Phase**: Assessment
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- **Target Audience**: VRAM budget
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- **Confidence**: ⚠️ Medium (extrapolated from classification task)
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- **Related Dimension**: DINOv2 Model Selection
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## Fact #12
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- **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."
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- **Source**: [Source #8] AIN.ua, Google statements
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- **Phase**: Assessment
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- **Target Audience**: Satellite imagery freshness
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- **Confidence**: ✅ High
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- **Related Dimension**: Satellite Imagery Quality
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## Fact #13
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- **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.
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- **Source**: [Source #9] GTSAM GitHub #561
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- **Phase**: Assessment
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- **Target Audience**: Factor graph robustness
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- **Confidence**: ✅ High
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- **Related Dimension**: GTSAM Robustness
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## Fact #14
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- **Statement**: No independent reproduction of LiteSAM results exists. Search results often confuse LiteSAM (feature matcher) with Lite-SAM (ECCV 2024 segmentation model).
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- **Source**: [Source #1] Research verification
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- **Phase**: Assessment
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- **Target Audience**: Production readiness
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- **Confidence**: ✅ High
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- **Related Dimension**: LiteSAM Maturity
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## Fact #15
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- **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).
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- **Source**: Calculated from Sources #1, #3, #7
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- **Phase**: Assessment
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- **Target Audience**: VRAM budget
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- **Confidence**: ⚠️ Medium (estimated)
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- **Related Dimension**: VRAM Budget
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## Fact #16
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- **Statement**: Maxar restored satellite imagery access for Ukraine in March 2025 (was suspended). Commercial, paid-only. 31-50cm resolution (WorldView, GeoEye).
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- **Source**: [Source #12] Defense Express
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- **Phase**: Assessment
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- **Target Audience**: Alternative satellite providers
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- **Confidence**: ✅ High
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- **Related Dimension**: Satellite Imagery Quality
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## Fact #17
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- **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.
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- **Source**: [Source #11] Tracasa
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- **Phase**: Assessment
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- **Target Audience**: Alternative satellite sources
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- **Confidence**: ⚠️ Medium (2.5m resolution vs required 0.3-0.5m)
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- **Related Dimension**: Satellite Imagery Quality
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