gps-denied-desktop/_docs/00_research/draft04_assessment/02_fact_cards.md

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